“Disadvantaged patient populations”: A theory-informed education needs assessment in an urban teaching hospital
Bibliographic record
Abstract
Recent calls in medical education and health care emphasize equitable care for disadvantaged patient populations (DPP), with education highlighted as a key mechanism to move toward this goal. However, in order to develop effective education strategies we must first better understand the DPP concept. We conducted a theory-informed needs assessment to explore the concept of DPP as understood in our hospital. Using an interpretive qualitative approach informed by principles of critical discourse analysis we conducted focus groups with trainees and staff across professions and groups, as identified in the hospital’s strategic plan, representing “patients experiencing disadvantage.” We identified three main perceptions about DPP: 1) disadvantaged patients require care above and beyond what is normal; 2) the system is to blame for failures in serving disadvantaged patients; and 3) labelling patients is problematic and stigmatizing. In response, patients wanted to be first seen as valuable human beings rather than as a burden or category. Patients appreciated that the DPP concept opened up better access to care, but also felt ‘othered’ by the concept. As a result, patients felt they were not accessing the same level of care in terms of compassion and respect. Our findings suggest potential for three, theory-informed educational approaches to help improve care for patients experiencing disadvantage: 1) sharing authentic and varied stories; 2) fostering dialogue; and 3) aligning assessment approaches with educational approaches. Additionally, we suggest a need to define access beyond the ability to receive services; according to our participants, access must also engender a sense of common humanity and respect.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".