Work in progress. Integrating physicians' services in the home.
Bibliographic record
Abstract
OBJECTIVE: While increasing acuity levels and the concomitant complexity of service demand that physicians be involved in in-home care, conflicting evidence and opinions do not show how this can best be achieved. DESIGN: A phenomenologic research design was used to obtain insights into the challenges and opportunities of integrating physicians' services into the usual in-home services in London, Ont. SETTING: Home care in London, Ont. PARTICIPANTS: Twelve participants included three patients, two family caregivers, two family physicians, the program's nurse practitioner, two case managers, and two community nurses. METHOD: In-depth interviews with a maximally varied purposeful sample of patients, caregivers, and providers were analyzed using immersion and crystallization techniques. MAIN FINDINGS: Findings revealed the potential for enhanced continuity of care and interdisciplinary team functioning. Having a nurse practitioner, interdisciplinary team-building exercises and meetings, regular face-to-face contact among all providers, support for family caregivers, and 24-hour coverage for physicians were found to be essential for success. CONCLUSION: Integration of services takes time, money, and sustained commitment, particularly when undertaken in geographically isolated communities. Informed choice and a fair remuneration system remain important considerations for family physicians.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".