Martin's Map: a conceptual framework for teaching and learning the medical interview using a patient‐centred approach
Bibliographic record
Abstract
OBJECTIVE: At the end of training, students seem to lack a basic understanding of how to take an organised, relevant medical and social history using a patient-centred approach. The aim of developing the map described in this paper was to provide a framework for such an approach. METHODS: Action research was used to continuously modify and refine an interview map that was used by medical clerks, family medicine residents, international medical graduates and practising doctors for teaching and learning purposes over a 10-year period. CONCLUSION: 'Martin's Map' provides a realistic framework for flexibly organising and integrating medical content with process that did not previously exist. The map provides medical educators with a standardised framework for talking about the medical interview, which helps learners understand how to use their medical knowledge with a patient-centred approach. Learners are able to visually see how they can take a focused medical and social history using a patient-centred approach, which subsequently seems to help them organise their thinking and approach during the medical encounter.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".