Learners’ Perspectives of Professionalism: Protocol for a Mixed Methods Systematic Review
Bibliographic record
Abstract
BACKGROUND: Professionalism has come to be associated with competence in medical education, with the habitual and judicious use of communication, knowledge, technical skills, clinical reasoning, emotions, values, and reflection in daily practice for the benefit of the individual and community being served. Recent studies indicate students should have the opportunity to observe the application of knowledge and skills by their mentors to improve patient health and safety. A noticeable detail that needs implementation into the curriculum is the inclusion of student perspectives. This review will explore students' understanding and experience of professionalism in undergraduate medical education (UME). OBJECTIVE: This paper presents the protocol for a review that aims to develop an integrated synthesis of qualitative and quantitative studies resulting in recommendations for medical school curricula to incorporate the learners' perspectives in teaching professionalism in UME. METHODS: We will take an integrated approach to synthesis. Data will be extracted from the included studies, and quantitative data will be "qualitized." PubMed (Medline), Embase, PsycInfo, and ERIC (Education Resources Information Center) will be searched for studies published in English from 2010 to 2021. Studies will be screened and critically appraised for methodological quality using the Mixed Methods Appraisal Tool by 2 researchers, with disagreements resolved by a third researcher. Qualitative, quantitative, and mixed methods studies will be considered. Our population of interest is undergraduate medical students; hence, studies on medical residents and graduate medical students will be excluded. We will consider studies that explore how concepts of professionalism are understood, experienced, and taught in undergraduate medicine and on how medical students understand and develop the identified constructs of professionalism. RESULTS: This study is in the screening phase; therefore, no results are available at this time. However, we had initiated the searches, screening, and are currently in the critical appraisal stage. We will commence preparation to clean and convert the data for coding in July 2022, and analysis will be ongoing from the end of July 2022 until submission for publication in November 2022. CONCLUSIONS: This research will contribute to the student perspectives on professionalism in medical education literature. The findings will aid in the creation of a checklist to guide the development of a curriculum on professionalism in UME. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37473.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.169 | 0.191 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.022 | 0.019 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".