Being honest with causal language in writing for publication

Avoidance of causality outside experiments: Hypotheses from cognitive dissonance reduction

The avoidance of causality in the design, analysis and interpretation of non-experimental studies has often been criticised as an untenable scientific stance, because theories are based on causal relations (and not associations) and a rich set of methodological tools for causal analysis has been developed in recent decades. Psychology researchers (n = 106 with complete data) participated in an online study presenting a causal statement about the results of a fictitious paper on the potential effect of drinking clear water for years on the risk of dementia. Two randomised groups of participants were then asked to reflect on the conflict between the goal of approaching a causal answer and the prevailing norm of avoiding doing so. One of the two groups was also instructed to think about possible benefits of addressing causality. Both groups then responded to a list of 19 items about attitudes to causal questions in science. A control group did this without reflecting on conflict or benefits. Free-text assessments were also collected during reflection, giving some indication of how and why causality is avoided. We condense the exploratory findings of this study into five new hypotheses about the how and why, filtered through what can be explained by cognitive dissonance reduction theory. These concern the cost of addressing causality, the variety of ways in which dissonance can be reduced, the need for profound intervention through teaching and social aspects. Predictions are derived from the hypotheses for confirmation trials in future studies and recommendations for teaching causality. Open data are provided for researchers' own analyses.

Modeling State Firearm Law Adoption Using Temporal Network Models

Policy Points Promoting healthy public policies is a national priority, but state policy adoption is driven by a complex set of internal and external factors. This study employs new social network methods to identify underlying connections among states and to predict the likelihood of new firearm-related policy adoption given changes to this interstate network. This approach could be used to assess the likelihood that a given state will adopt a specific new firearm-related law and to identify points of influence that could either inhibit or promote wider diffusion of specific laws.

CONTEXT

US states are largely responsible for the regulation of firearms within their borders. Each state has developed a different legal environment with regard to firearms based on different values and beliefs of citizens, legislators, governors, and other stakeholders. Predicting the types of firearm laws that states may adopt is therefore challenging.

METHODS

We propose a parsimonious model for this complex process and provide credible predictions of state firearm laws by estimating the likelihood they will be passed in the future. We employ a temporal exponential-family random graph model to capture the bipartite state law-state network data over time, allowing for complex interdependencies and their temporal evolution. Using data on all state firearm laws over the period 1979-2020, we estimate these models' parameters while controlling for factors associated with firearm law adoption, including internal and external state characteristics. Predictions of future firearm law passage are then calculated based on a number of scenarios to assess the effects of a given type of firearm law being passed in the future by a given state.

FINDINGS

Results show that a set of internal state factors are important predictors of firearm law adoption, but the actions of neighboring states may be just as important. Analysis of scenarios provide insights into the mechanics of how adoption of laws by specific states (or groups of states) may perturb the rest of the network structure and alter the likelihood that new laws would become more (or less) likely to continue to diffuse to other states.

CONCLUSIONS

The methods used here outperform standard approaches for policy diffusion studies and afford predictions that are superior to those of an ensemble of machine learning tools. The proposed framework could have applications for the study of policy diffusion in other domains.

The "Why" in Mental Health, Stigma, and Addictive Behaviors: Causal Inferences in Applied Settings

Mental health problems, broadly understood, are highly prevalent [...].

The use of causal language and inferences in observational shoulder arthroplasty database studies

BACKGROUND

Over the last few decades, several databases and clinical registries have been used to generate studies for orthopedic surgery research including shoulder arthroplasty. Observational database studies present data that are best used to provide information regarding associations between variables but is unable to prove causation. Yet, there remains a tendency for authors to use causal language and conclusive statements in published shoulder arthroplasty literature using such databases.

METHODS

We systematically identified administrative database and registry studies on shoulder arthroplasty in 5 orthopedic journals from January 2020 to December 2021. Papers were independently graded by 2 reviewers for the presence, absence, or inconsistent use of causal language throughout the publication. χ2 analyses were conducted to determine the relationship between causality language and article characteristics including the journal of publication.

RESULTS

There were 65 eligible articles. According to the reviewers, 67.7% of titles and abstracts were either consistently causal or inconsistently causal in language and inference. On reviewing the full text, 69.2% were found to contain some degree of causal language (38.5% consistently causal, 30.8% inconsistently causal). There were no statistically significant associations between the journal and title and abstract final grading (P = .863) or the journal and full-text grading (P = .726).

CONCLUSION

The use of causal language remains highly prevalent in observational database studies related to shoulder arthroplasty. Understanding the limitations of observational database studies is paramount for accurate interpretation of these publications.

Internal and International Migration and the Mental Health of “Left-Behind” Older Parents

Despite evidence of the impact of adult children's migration on left-behind older parents, the comparison of left-behind older parents’ mental health and psychological well-being between internal and international migration is limited. This article examines how the relationship between adult children's migration and parent's mental health differs according to the migrant's destination — only internal migration, only international migration, or hybrid migration (at least one child who migrated internally and at least one child who migrated internationally). Drawing on a 2019 cross-sectional population-based survey of 697 left-behind older adults (≥ 60 years) in Nepal, we assessed the mental health of older parents, using the 21-item Depression Anxiety Stress Scales and a range of socio-demographic, health, lifestyle, and child-related characteristics. Multilevel mixed-method linear regression was performed to examine the effect of adult children's migration on left-behind older parents’ depression, anxiety, and stress symptoms. Results showed that parents whose children had all migrated internationally were at higher risk of mental health problems, particularly anxiety and stress, compared to parents whose children had all migrated internally. Older parents from the hybrid group (having both internal and international migrant adult children) had higher levels of anxiety, compared to parents whose children had all migrated internally, with no significant difference in depression and stress symptoms. Further, mothers of internationally migrated children were at higher risk of mental health symptoms than fathers. Interventions aiming to improve left-behind older parents’ mental health and psychological well-being should specifically target parents whose children have migrated internationally.

Mixed messages: most spinal pain and osteoarthritis observational research is unclear or misaligned

OBJECTIVES

We assessed authors' language and methods to determine alignment between reported aims, methods, intent, and interpretations in observational studies in spinal pain or osteoarthritis.

STUDY DESIGN AND SETTING

We searched five databases for observational studies that included people with spinal pain or osteoarthritis published in the last 5 years. We randomized 100 eligible studies, and classified study intent (aims and methods) and interpretations as causal, non-causal, unclear, or misaligned.

RESULTS

Overall, 38% of studies were aligned regarding their intent and interpretation (either causally (22%) or non-causally (16%)). 29% of studies' aims and 29% of study methods were unclear. Intent was misaligned in 16% of studies (where aim differed to method) and 23% of studies had misaligned interpretations (where there were multiple conflicting claims). The most common kind of aim was non-causal (38%), and the most common type of method (39%), intent (38%), and interpretations (35%) was causal.

CONCLUSIONS

Misalignment and mixed messages are common in observational research of spinal pain and osteoarthritis. More than 6 in 10 observational studies may be uninterpretable, because study intent and interpretations do not align. While causal methods and intent are most common in observational research, authors commonly shroud causal intent in non-causal terminology.

High Prevalence of Causal Language and Inferences in Observational Hip and Knee Arthroplasty Database Studies: A Review of Papers Published Across Four Orthopaedic Journals

BACKGROUND

The use of administrative databases and clinical registries in lower extremity arthroplasty research is growing. Such observational studies are unable to fully control for confounders and cannot establish causality. However, many authors use causal language when describing their aims or findings, potentially misleading readers. We examined the prevalence of causal language and inferences in the lower extremity arthroplasty literature.

METHODS

We systematically identified administrative database and registry studies on hip and knee arthroplasty that were published in 4 orthopaedic journals in 2020. Articles were graded independently by two reviewers for the presence of causal language in both the title and abstract and the full text. Chi-squared analyses were conducted to determine the relationship between the causality grading and article characteristics including the journal of publication.

RESULTS

Of 116 eligible articles, we classified 79.3% of titles and abstracts as either consistently causal or inconsistently causal, with only 20.7% as consistently noncausal. A total of 40.5% of full texts were consistently causal, 49.1% were inconsistent, and 10.3% were consistently noncausal. Chi-squared analyses revealed no statistically significant association between the title and abstract's grading and the journal (P = .720) nor with the use of a database or registry (P = .716).

CONCLUSION

Causal language and inferences were present in 79.3% of titles and abstracts of lower extremity arthroplasty observational database studies published in 2020. The high prevalence of causal language and inferences in the arthroplasty literature may mislead readers.

Causal and Associational Language in Observational Health Research: A Systematic Evaluation

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as none (no causal implication) in 13.8%, weak in 34.2%, moderate in 33.2%, and strong in 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was "associate" (45.7%). Reviewers' ratings of linking word roots were highly heterogeneous; over half of reviewers rated "association" as having at least some causal implication. This research undercuts the assumption that avoiding "causal" words leads to clarity of interpretation in medical research.

Watch your language: An exploration of the use of causal wording in veterinary observational research

Observational research may be conducted to predict an outcome or to identify associations between an intervention or risk factor (an “exposure”) and an outcome. However, the end goal of observational research often is to identify exposures that can be manipulated to improve an outcome, meaning that the aim is identify causal relationships. Causal inference from observational studies may be appropriate when an exposure-outcome of interest is identified, causal reasoning is used to identify confounders, confounders are adequately controlled, and theoretical issues, such as temporality, are considered. If these conditions are not met, causal inference cannot be made in an observational study. The objective of our study was to explore the use of causal language in veterinary observational studies, and to compare the use of causal language between studies that appear to be predictive or associational in purpose vs. those that appear to be exploring causal relationships. The dataset comprised 200 observational studies in veterinary species published between 2020 and 2022. The majority (117 out of 200) were cross-sectional studies. There were 48 studies that described an exposure-outcome of interest, and we considered these studies to be exploring potential causal relationships; of note, this liberal categorization would be anticipated to overestimate the proportion of studies suitably designed for causal inference. Overall, 172 studies (86%) used causal wording in at least one section of the article. Causal language was used in 128/152 (84%) of studies exploring predictions or associations; this language implies causation when it is not appropriate to do so. In studies designed such that causal inference might be possible, 44/48 (92%) used causal language in one or more sections. There were no substantive differences in the use of causal wording between observational study designs, exposure types, or whether the first author's affiliation was a country in which English is an official language. There is a need for authors of veterinary observational studies to explicitly state the purpose of the study (associational, predictive, or causal), and to use causal wording appropriately based on the aim of the study.

Antecedents of unfinished nursing care: a systematic review of the literature

BACKGROUND

Unfinished Nursing Care (UNC) concept, that express the condition when nurses are forced to delay or omit required nursing care, has been largely investigated as tasks left undone, missed care, and implicit rationing of nursing care. However, no summary of the available evidence regarding UNC antecedents has been published. The aim of this study is to identify and summarise antecedents of UNC as documented in primary studies to date.

METHODS

A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. MEDLINE, CINAHL, SCOPUS, and PROSPERO databases were searched for quantitative studies reporting the relationships between antecedents and UNC published after 2004 up to 21 January 2020. The reference lists of secondary studies have been scrutinised to identify additional studies. Two reviewers independently identified studies and evaluated them for their eligibility and disagreements were resolved by the research team. The quality appraisal was based on the Joanna Briggs Institute Critical Appraisal tools, according to the study designs. A data extraction grid was piloted and then used to extract data. The antecedents that emerged were thematically categorised with an inductive approach.

RESULTS

Fifty-eight studies were included; among them, 54 were cross-sectional, three were cohort studies, and one was a quasi-experimental study. They were conducted mainly in the United States and in hospital settings. The UNC antecedents have been investigated to date at the (a) unit (e.g., workloads, non-nursing tasks), (b) nurse (e.g., age, gender), and (c) patient levels (clinical instability).

CONCLUSIONS

At the unit level, it is highly recommended to provide an adequate staff level, strategies to deal with unpredictable workloads, and to promote good practice environments to reduce or minimise UNC. By contrast, at the nurse and patient levels, there were no clear trends regarding modifiable factors that could decrease the occurrence of UNC. The map of antecedents that emerged can be used to design interventional studies aimed at changing research from merely descriptive to that which evaluates the effectiveness of interventions.

Conducting Systematic Reviews of the Quality and Psychometric Properties of Health-Related Measurement Instruments: Finding the Right Tool for the Job

Many health-related measurement instruments have been developed to measure psychological constructs and whilst several instruments are usually available for a particular study, finding the right tool for the job is important. Systematic reviews of measurement properties of instruments have long been identified as a valuable strategy to ensure that we select the right tool to assess mental health. There are many important steps and procedures to guide these types of systematic reviews to find the "best fit" and this paper summarizes some of these key processes and steps. The selection of instrument(s) to use should be made considering the most recent comprehensive review of the quality of the outcome measurement instrument based on unbiased assessment of its psychometric properties, responsiveness, and generalizability of results. Researchers planning to conduct a systematic review of health-related measurement instruments should design the review beforehand using standardized frameworks. Conducting systematic reviews of the quality and psychometric properties of health-related measurement instruments is important to ensure we choose the best tool for the research question and target population.

Causal Language in Observational Orthopaedic Research

With the increasing availability of large clinical registries and administrative data sets, observational (i.e., nonexperimental) orthopaedic research is being performed with increased frequency. While this research substantially advances our field, there are fundamental limitations to what can be determined through a single observational study. Avoiding overstatements and misstatements is important for the sake of accuracy, particularly for ensuring that clinical care is not inadvertently swayed by how an observational study is written up and described. We have noticed that causal language is frequently misused in observational orthopaedic research-that is, language that says or implies that 1 variable definitively causes another, despite the fact that causation can generally only be determined with randomization. In this data-backed commentary, we examine the prevalence of causal language in a random sample of 400 observational orthopaedic studies; we found that causal language was misused in 60% of them. We discuss the implications of these results and how to report observational findings more accurately: the word "association" (and its derivatives) can almost always replace or reframe a causal phrase.

Over-promotion and caution in abstracts of preprints during the COVID-19 crisis

The abstract is known to be a promotional genre where researchers tend to exaggerate the benefit of their research and use a promotional discourse to catch the reader's attention. The COVID-19 pandemic has prompted intensive research and has changed traditional publishing with the massive adoption of preprints by researchers. Our aim is to investigate whether the crisis and the ensuing scientific and economic competition have changed the lexical content of abstracts. We propose a comparative study of abstracts associated with preprints issued in response to the pandemic relative to abstracts produced during the closest pre-pandemic period. We show that with the increase (on average and in percentage) of positive words (especially effective) and the slight decrease of negative words, there is a strong increase in hedge words (the most frequent of which are the modal verbs can and may). Hedge words counterbalance the excessive use of positive words and thus invite the readers, who go probably beyond the 'usual' audience, to be cautious with the obtained results. The abstracts of preprints urgently produced in response to the COVID-19 crisis stand between uncertainty and over-promotion, illustrating the balance that authors have to achieve between promoting their results and appealing for caution.

Consistency of causal claims in observational studies: a review of papers published in a general medical journal

OBJECTIVE

To evaluate the consistency of causal statements in observational studies published in The BMJ.

DESIGN

Review of observational studies published in a general medical journal.

DATA SOURCE

Cohort and other longitudinal studies describing an exposure-outcome relationship published in The BMJ in 2018. We also had access to the submitted papers and reviewer reports.

MAIN OUTCOME MEASURES

Proportion of published research papers with 'inconsistent' use of causal language. Papers where language was consistently causal or non-causal were classified as 'consistently causal' or 'consistently not causal', respectively. For the 'inconsistent' papers, we then compared the published and submitted version.

RESULTS

Of 151 published research papers, 60 described eligible studies. Of these 60, we classified the causal language used as 'consistently causal' (48%), 'inconsistent' (20%) and 'consistently not causal'(32%). Eleven out of 12 (92%) of the 'inconsistent' papers were already inconsistent on submission. The inconsistencies found in both submitted and published versions were mainly due to mismatches between objectives and conclusions. One section might be carefully phrased in terms of association while the other presented causal language. When identifying only an association, some authors jumped to recommending acting on the findings as if motivated by the evidence presented.

CONCLUSION

Further guidance is necessary for authors on what constitutes a causal statement and how to justify or discuss assumptions involved. Based on screening these papers, we provide a list of expressions beyond the obvious 'cause' word which may inspire a useful more comprehensive compendium on causal language.

Prevalence and Socio-Demographic Predictors of Food Insecurity in Australia during the COVID-19 Pandemic

The COVID-19 pandemic has exacerbated economic vulnerabilities and disrupted the Australian food supply, with potential implications for food insecurity. This study aims to describe the prevalence and socio-demographic associations of food insecurity in Tasmania, Australia, during the COVID-19 pandemic. A cross-sectional survey (deployed late May to early June 2020) incorporated the U.S. Household Food Security Survey Module: Six-Item Short Form, and fifteen demographic and COVID-related income questions. Survey data (n = 1170) were analyzed using univariate and multivariate binary logistic regression. The prevalence of food insecurity was 26%. The adjusted odds of food insecurity were higher among respondents with a disability, from a rural area, and living with dependents. Increasing age, a university education, and income above $80,000/year were protective against food insecurity. Food insecurity more than doubled with a loss of household income above 25% (Adjusted Odds Ratio (AOR): 2.02; 95% CI: 1.11, 3.71; p = 0.022), and the odds further increased with loss of income above 75% (AOR: 7.14; 95% CI: 2.01, 24.83; p = 0.002). Our results suggest that the prevalence of food insecurity may have increased during the COVID-19 pandemic, particularly among economically vulnerable households and people who lost income. Policies that support disadvantaged households and ensure adequate employment opportunities are important to support Australians throughout and post the COVID-19 pandemic.