The perception of continuity of care from the perspective of patients with diabetes.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Traditional indices of continuity of care typically capture frequency of physician visits but lack information regarding how patients themselves perceive continuity of care. The present study's objectives were (1) to examine the meaning of continuity of care from the perspective of patients with diabetes and (2) to understand the factors that enhance or detract from continuity of care. METHODS: Seven focus groups with 46 adult patients were held at a health service organization in Northern Ontario. All focus group interviews were tape recorded, transcribed verbatim, and analyzed using a phenomenological approach. Triangulation occurred through participant feedback of transcript summaries and consensus of themes by the multidisciplinary research team. RESULTS: Patients conceptualized continuity of care in a broad and multifaceted manner that was comprised of five components: (1) access to services, (2) interactions with physician, (3) interactions with other health care providers, (4) personal self responsibility, and (5) communication. CONCLUSIONS: Continuity of care was perceived by patients to include a wider range of components than what is traditionally associated with continuity of care. The emphasis on personal self responsibility by some patients provides a deeper understanding of what patients feel encompass continuity of care.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".