Why does continuity of care with family doctors matter?
Bibliographic record
Abstract
OBJECTIVE: To summarize and synthesize qualitative studies that report patient and physician perspectives on continuity of care in family practice. DATA SOURCES: MEDLINE (Ovid), EMBASE (Ovid), and PsycInfo (Ovid) were searched for qualitative primary research reporting perspectives of patients, physicians, or both, on continuity of care in family practice. STUDY SELECTION: English-language qualitative studies were selected (eg, interviews, focus groups, mixed methods) that were conducted in Canada, the United States, the United Kingdom, the European Union, New Zealand, or Australia. SYNTHESIS: Themes were extracted, summarized, and synthesized. Six overarching themes emerged: continuity of care enables person-centred care; continuity of care increases quality of care; continuity of care leads to greater confidence in medical decision making; continuity of care comes with drawbacks; the absence of continuity of care may lead to medical and psychological harm; and continuity of care can foster greater joy and meaning in a physician's work. Out of the 6 themes, patients and physicians shared the first 5. CONCLUSION: To the authors' knowledge, this is the first qualitative review reporting the unique perspectives of both patients and family physicians on continuity of care. The findings add nuanced insight to the importance of continuity of care in family practice.
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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 | 0.061 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".