From helplessness to transformation: An analysis of clinician narratives about the social determinants of health and their implications for training and practice
Bibliographic record
Abstract
BACKGROUND: Medical curricula are attempting to prepare trainees to address the social determinants of health, however the life circumstances of patients are often beyond physician control. Little is known about how physicians cope with this dilemma; we sought to examine their perspectives when faced with this challenge to help better prepare trainees for practice. METHODS: We undertook a critical analysis of physician narratives from January 2018 to June 2020. In total, 268 physician-written narrative social determinant of health pieces from four high impact medical journals were screened and 47 met the inclusion criteria and were analysed. RESULTS: We identified four storylines that described the physician experience and strategies for coping with the social determinants of health. While Helplessness stories described authors' experiences of emotional distress when unable to support their patients, the other story types described ways they could make a difference. In Shortcoming and Transformation stories, the realisations about shortcomings led to transformation. In Doctor-patient relationship stories, authors described its importance in theirs and patients' lives, and in System advocacy stories, they described the need for greater advocacy to help change broken systems. CONCLUSIONS: Current approaches to teaching the social determinants of health often focus on the role of physicians in recognising and altering social circumstances. However, the realities of practice do not easily allow physicians to do so and, for some, may lead to distress and burnout. There are other ways to cope and make a difference by improving ourselves, investing in getting to know our patients, and advocating. These results can help better support trainees and physicians for the realities of practice.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".