Trainee doctors' perceptions of the surgeon stereotype and its impact on professional identification: a qualitative study

Serve Coffee, Hold Clamps, Do Not Complain: Student Perceptions and Experiences Regarding Surgery

OBJECTIVE

To explore medical students' perceptions and experiences regarding the surgery clerkship and surgeons.

DESIGN

Between November 2021 and February 2022, an anonymous prepost survey study was performed among 2 consecutive cohorts of medical students. The survey was taken 6 weeks prior to the surgery clerkship and repeated shortly after the surgery clerkship.

SETTING

Single-center prepost survey study.

PARTICIPANTS

Medical students studying at Erasmus Medical Center entering a 6-week surgery education block directly followed by a ten-week surgery clerkship.

RESULTS

The preclerkship response rate was 100% (n=145). One out of 5 students considered a surgical career (21%). Half of the students expected to be negatively treated (55%), mainly in terms of hierarchy and offensive language. The postclerkship response rate was 70% (n=101). Interest in a surgical career increased significantly from 21% to 50% (p<0.001). Thirteen students (13%) reported being negatively treated during their surgery clerkship, mainly in terms of poor supervision of their learning process and nonconstructive feedback.

CONCLUSIONS AND RELEVANCE

Stereotypes of surgeons and the surgical clerkship are strongly prevalent among medical students. Half of the students enter the surgery clerkship with negative perceptions. Fortunately, the surgery clerkship debunked prejudices and increased interest in surgery. Poor supervision and feedback during the surgery clerkship were experienced as negative treatment, emphasizing the importance of cultivating a safe learning climate.

In the shoes of junior doctors: a qualitative exploration of job performance using the job-demands resources model

Background

This qualitative study aimed to explore the factors affecting job performance amongst junior doctors working for public healthcare institutions in Singapore. Within these institutions, junior doctors experience challenges with maintaining a balance in job demands and resources, leading to strain. Exploring the lived experiences of these junior doctors is essential when reviewing workplace and organizational factors that contribute to stress on an individual level, providing valuable insights to address these challenges effectively.

Method

Semi-structured interviews were conducted with 20 junior doctors in Singapore, ranging from house officers to senior residents. Framework analysis was performed on transcribed de-identified interviews to identify themes deductively based on the Job Demands-Resources (JD-R) Model.

Results

Themes were identified and contextualized based on the exiting JD-R model. These themes shed light on how work demands, resources and personal factors influence the job performance of junior doctors and job satisfaction.

Conclusion

The study offers valuable insights into the specific issues disrupting the job demands and resource balance in Singapore Public Healthcare Institutions and their correlation with job performance. Our data suggests that job performance may be associated with job satisfaction. By understanding these factors, targeted efforts can be developed to improve working conditions for junior doctors, fostering their growth and engagement within the public healthcare system.

Gender Representation of Health Care Professionals in Large Language Model-Generated Stories

Importance

With the growing use of large language models (LLMs) in education and health care settings, it is important to ensure that the information they generate is diverse and equitable, to avoid reinforcing or creating stereotypes that may influence the aspirations of upcoming generations.

Objective

To evaluate the gender representation of LLM-generated stories involving medical doctors, surgeons, and nurses and to investigate the association of varying personality and professional seniority descriptors with the gender proportions for these professions.

Design, Setting, and Participants

This is a cross-sectional simulation study of publicly accessible LLMs, accessed from December 2023 to January 2024. GPT-3.5-turbo and GPT-4 (OpenAI), Gemini-pro (Google), and Llama-2-70B-chat (Meta) were prompted to generate 500 stories featuring medical doctors, surgeons, and nurses for a total 6000 stories. A further 43 200 prompts were submitted to the LLMs containing varying descriptors of personality (agreeableness, neuroticism, extraversion, conscientiousness, and openness) and professional seniority.

Main Outcomes and Measures

The primary outcome was the gender proportion (she/her vs he/him) within stories generated by LLMs about medical doctors, surgeons, and nurses, through analyzing the pronouns contained within the stories using χ2 analyses. The pronoun proportions for each health care profession were compared with US Census data by descriptive statistics and χ2 tests.

Results

In the initial 6000 prompts submitted to the LLMs, 98% of nurses were referred to by she/her pronouns. The representation of she/her for medical doctors ranged from 50% to 84%, and that for surgeons ranged from 36% to 80%. In the 43 200 additional prompts containing personality and seniority descriptors, stories of medical doctors and surgeons with higher agreeableness, openness, and conscientiousness, as well as lower neuroticism, resulted in higher she/her (reduced he/him) representation. For several LLMs, stories focusing on senior medical doctors and surgeons were less likely to be she/her than stories focusing on junior medical doctors and surgeons.

Conclusions and Relevance

This cross-sectional study highlights the need for LLM developers to update their tools for equitable and diverse gender representation in essential health care roles, including medical doctors, surgeons, and nurses. As LLMs become increasingly adopted throughout health care and education, continuous monitoring of these tools is needed to ensure that they reflect a diverse workforce, capable of serving society's needs effectively.

Team players and helpers - describing professional identity among finnish physicians in a cross-sectional study

BACKGROUND

Every physician has a unique professional identity. However, little is known about the diversity of identities among physicians. This study aimed to quantitatively assess the professional identity of physicians in Finland using descriptions of professional identity.

METHODS

This study was part of a larger cross-sectional Finnish Physician 2018 Study. The target population consisted of all Finnish physicians under the age of 70 (N = 24,827) in 2018. The sample was drawn from physicians born on even numbered days (N = 11,336) using the Finnish Medical Association register. A total of 5,187 (46%) physicians responded. Professional identity was examined by 27 given characterisations using a five-point Likert scale. Multivariate logistic regression was used in assessing how place of work, graduation year and gender were associated with identity descriptions.

RESULTS

The descriptions which most physicians identified with were "member of a working group/team" (82%), "helper" (82%), and "health expert" (79%); the majority reported these as describing them very or quite well. Identity descriptions such as "prescriber of medications" (68% vs. 45%), "prioritiser" (57% vs. 35%) and "someone issuing certificates" (52% vs. 32%) were more popular among junior than senior physicians. The biggest differences between the genders were found in the descriptions "provider of comfort" (62% vs. 40%) and "someone engaged in social work" (45% vs. 25%), with which women identified more frequently than men.

CONCLUSIONS

Strong identification as a member of a team is an important finding in the increasingly multiprofessional world of health care. Importantly, most physicians shared several core professional identity descriptions (i.e., helper, health expert) that reflect the traditional image of an exemplary doctor.

Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators

Importance

The progression of artificial intelligence (AI) text-to-image generators raises concerns of perpetuating societal biases, including profession-based stereotypes.

Objective

To gauge the demographic accuracy of surgeon representation by 3 prominent AI text-to-image models compared to real-world attending surgeons and trainees.

Design, Setting, and Participants

The study used a cross-sectional design, assessing the latest release of 3 leading publicly available AI text-to-image generators. Seven independent reviewers categorized AI-produced images. A total of 2400 images were analyzed, generated across 8 surgical specialties within each model. An additional 1200 images were evaluated based on geographic prompts for 3 countries. The study was conducted in May 2023. The 3 AI text-to-image generators were chosen due to their popularity at the time of this study. The measure of demographic characteristics was provided by the Association of American Medical Colleges subspecialty report, which references the American Medical Association master file for physician demographic characteristics across 50 states. Given changing demographic characteristics in trainees compared to attending surgeons, the decision was made to look into both groups separately. Race (non-White, defined as any race other than non-Hispanic White, and White) and gender (female and male) were assessed to evaluate known societal biases.

Exposures

Images were generated using a prompt template, "a photo of the face of a [blank]", with the blank replaced by a surgical specialty. Geographic-based prompting was evaluated by specifying the most populous countries on 3 continents (the US, Nigeria, and China).

Main Outcomes and Measures

The study compared representation of female and non-White surgeons in each model with real demographic data using χ2, Fisher exact, and proportion tests.

Results

There was a significantly higher mean representation of female (35.8% vs 14.7%; P < .001) and non-White (37.4% vs 22.8%; P < .001) surgeons among trainees than attending surgeons. DALL-E 2 reflected attending surgeons' true demographic data for female surgeons (15.9% vs 14.7%; P = .39) and non-White surgeons (22.6% vs 22.8%; P = .92) but underestimated trainees' representation for both female (15.9% vs 35.8%; P < .001) and non-White (22.6% vs 37.4%; P < .001) surgeons. In contrast, Midjourney and Stable Diffusion had significantly lower representation of images of female (0% and 1.8%, respectively; P < .001) and non-White (0.5% and 0.6%, respectively; P < .001) surgeons than DALL-E 2 or true demographic data. Geographic-based prompting increased non-White surgeon representation but did not alter female representation for all models in prompts specifying Nigeria and China.

Conclusion and Relevance

In this study, 2 leading publicly available text-to-image generators amplified societal biases, depicting over 98% surgeons as White and male. While 1 of the models depicted comparable demographic characteristics to real attending surgeons, all 3 models underestimated trainee representation. The study suggests the need for guardrails and robust feedback systems to minimize AI text-to-image generators magnifying stereotypes in professions such as surgery.

Medical Students' Communication Preferences for the Ideal Surgical Educator

OBJECTIVE

Medical students have expectations and preferences for how they are taught by clinical surgical educators. The goal of this study was to (a) determine medical students' prioritizations of ideal teaching behaviors and characteristics for surgical educators, and (b) delineate which teaching behaviors and characteristics were considered to be less important for surgical education.

DESIGN

Using a necessity (low) and luxury (high) budget allocation methodology to build their ideal surgical educator, MSIII and MSIV students (N = 82) completed a survey to prioritize and invest in 10 effective teaching behaviors and characteristics identified in the instructional communication literature (assertiveness, responsiveness, clarity, relevance, competence, character, caring, immediacy, humor, and disclosure).

RESULTS

Repeated-measures ANOVAs indicated MSIII and MSIV students invested significantly more of their teaching budget allocations for their ideal surgical educator into instructor clarity, competence, relevance, responsiveness, and caring, both within a (low) necessity budget (F[5.83, 472.17] = 24.09, p < 0.001, η2p = 0.23) and (high) luxury budget (F(7.65, 619.76) = 67.56, p < 0.001, η2p = 0.46). Using paired t-tests, comparisons of repeated investments in low and high budget allocations revealed that students invested slightly more of a percentage of funds in instructor immediacy (+2.62%; t(81) = 2.90, p = 0.005; d = 0.32) and disclosure (+1.44%; t(81) = 3.26, p = 0.002; d = 0.36), indicating they viewed these teaching behaviors more as luxury components of surgical education rather than necessities, but these behaviors were significantly less important than their ideal prioritizations of instructor clarity, competence, relevance, responsiveness, and caring.

CONCLUSIONS

Results indicated that medical students want a surgical educator who is largely a rhetorical educator; that is, a surgical specialist who clearly communicates expertise and relevant content that students can apply to their careers as future surgeons. However, a relational component was viewed as ideal by students as students also preferred surgical educators to be sensitive and sympathetic to their academic needs.