Integrating Physician Services in the Home: evaluation of an innovative program.
Bibliographic record
Abstract
OBJECTIVE: To evaluate a new program, Integrating Physician Services in the Home (IPSITH), to integrate family practice and home care for acutely ill patients. DESIGN: Causal model, mixed-method, multi-measures design including comparison of IPSITH and non-IPSITH patients. Data were collected through chart reviews and through surveys of IPSITH and non-IPSITH patients, caregivers, family physicians, and community nurses. SETTING: London, Ont, and surrounding communities, where home care is coordinated through the Community Care Access Centre. PARTICIPANTS: A total of 82 patients receiving the new IPSITH program of care (including 29 family physicians and 1 nurse practitioner), 82 non-randomized matched patients receiving usual care (and their physicians), community nurses, and caregivers. MAIN OUTCOME MEASURES: Emergency department (ED) visits and satisfaction with care. Analysis included a process evaluation of the IPSITH program and an outcomes evaluation comparing IPSITH and non-IPSITH patients. RESULTS: Patients and family physicians were very satisfied with the addition of a nurse practitioner to the IPSITH team. Controlling for symptom severity, a significantly smaller proportion of IPSITH patients had ED visits (3.7% versus 20.7%; P = .002), and IPSITH patients and their caregivers, family physicians, and community nurses had significantly higher levels of satisfaction (P < .05). There was no difference in caregiver burden between groups. CONCLUSION: Family physicians can be integrated into acute home care when appropriately supported by a team including a nurse practitioner. This integrated team was associated with better patient and system outcomes. The gains for the health system are reduced strain on hospital EDs and more satisfied patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".