Understanding the work of general practitioners: a social science perspective on the context of medical decision making in primary care
Bibliographic record
Abstract
BACKGROUND: The work of general practitioners (GPs) is increasingly being looked at from the perspective of the strategies and factors shaping it. This reflects the importance given to primary care services in health care system reform. However, the literature provides little insight into the medical decision-making processes in general practice. Our main objective was to better understand how organizational and environmental factors influence the work of GPs. METHODS: We interviewed 28 GPs working in contrasting organizational settings and environments. The data analysis involved using structuration theory to enrich the interpretation of empirical material. RESULTS: We identified four main factors that influence the practice of GPs: mode of remuneration, peer-to-peer interactions, patients' demands and the availability of other medical resources in the environment. These four conditions of action - what we call primary effects - can directly influence the performance of medical acts and time management, as well as the degree of specialization of GPs. Decisions related to each of those aspects can have a variety of both intentional and non-intentional consequences - what we call secondary effects - that are then likely to become conditions for subsequent action. CONCLUSION: This qualitative study helps shed light on the complex causal loops of interrelated factors that shape the work of GPs.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".