Health advocacy among medical learners: Unpacking contextual barriers and affordances
Bibliographic record
Abstract
INTRODUCTION: Learners and physicians are expected to practice as health advocates in Canadian contexts, but they rarely feel competent to practice this critical role when they complete their training. This is in part because advocacy is seen as "going above and beyond" routine practice and pushing the boundaries of systems that are resistant to change. Medical learning contexts are rife with barriers to learning about and practicing advocacy, and there is now a need to understand how contexts impact advocacy. METHODS: Using constructivist grounded theory study, we generated data through individual and group interviews with medical learners to explore the barriers and facilitators to advocacy in a variety of learning/practice contexts. We used purposeful and theoretical sampling to ensure that diverse learning contexts and learners who had different views on advocacy were represented. We constructed a theoretical model to understand advocacy decision-making through cycles of initial, focused and theoretical coding, using constant comparative analysis. RESULTS: Learners' thinking about health advocacy was framed by their own unique knowledge and beliefs, as well as their institutional and organisational contexts. With these influences in mind, learners made decisions about when to advocate within a local decision-making context, guided by affordances and barriers to advocacy involved in their perceptions of the patient, their own social position, resources available and social norms. CONCLUSIONS: This framework highlights critical aspects of context that influence learners' ability to learn about and practice as health advocates. If we are to adequately prepare learners for this important work, we must address aspects of their learning and practice contexts that make this work daunting, and we offer learners the tools required to intervene in contexts that do not support their efforts.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".