Primary health care models: medical students’ knowledge and perceptions.
Bibliographic record
Abstract
OBJECTIVE: To explore the knowledge and perceptions of fourth-year medical students regarding the new models of primary health care (PHC) and to ascertain whether that knowledge influenced their decisions to pursue careers in family medicine. DESIGN: Qualitative study using semistructured interviews. SETTING: The Schulich School of Medicine and Dentistry at The University of Western Ontario in London. Participants Fourth-year medical students graduating in 2009 who indicated family medicine as a possible career choice on their Canadian Residency Matching Service applications. METHODS: Eleven semistructured interviews were conducted between January and April of 2009. Data were analyzed using an iterative and interpretive approach. The analysis strategy of immersion and crystallization assisted in synthesizing the data to provide a comprehensive view of key themes and overarching concepts. MAIN FINDINGS: Four key themes were identified: the level of students’ knowledge regarding PHC models varied; the knowledge was generally obtained from practical experiences rather than classroom learning; students could identify both advantages and disadvantages of working within the new PHC models; and although students regarded the new PHC models positively, these models did not influence their decisions to pursue careers in family medicine. CONCLUSION: Knowledge of the new PHC models varies among fourth-year students, indicating a need for improved education strategies in the years before clinical training. Being able to identify advantages and disadvantages of the PHC models was not enough to influence participants’ choice of specialty. Educators and health care policy makers need to determine the best methods to promote and facilitate knowledge transfer about these PHC models.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".