Family physician enabling attitudes: a qualitative study of patient perceptions
Bibliographic record
Abstract
BACKGROUND: Family physicians frequently interact with people affected by chronic diseases, placing them in a privileged position to enable patients to gain control over and improve their health. Soliciting patients' perceptions about how their family physician can help them in this process is an essential step to promoting enabling attitudes among these health professionals. In this study, we aimed to identify family physician enabling attitudes and behaviours from the perspective of patients with chronic diseases. METHODS: We conducted a descriptive qualitative study with 30 patients, 35 to 75 years of age presenting at least one common chronic disease, recruited in primary care clinics in two regions of Quebec, Canada. Data were collected through in-depth interviews and were analyzed using thematic analysis. RESULTS: Family physician involvement in a partnership was perceived by participants as the main attribute of enablement. Promoting patient interests in the health care system was also important. Participants considered that having their situation taken into account maximized the impact of their physician's interventions and allowed the legitimization of their feelings. They found their family physician to be in a good position to acknowledge and promote their expertise, and to help them maintain hope. CONCLUSIONS: From the patient's perspective, their partnership with their family physician is the most important aspect of enablement.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".