Randomized controlled trial of Anticipatory and Preventive multidisciplinary Team Care
Bibliographic record
Abstract
OBJECTIVE T o examine whether quality of care (QOC) improves when nurse practitioners and pharmacists work with family physicians in community practice and focus their work on patients who are 50 years of age and older and considered to be at risk of experiencing adverse health outcomes. DESIGN Randomized controlled trial. SETTING A family health network with 8 family physicians, 5 nurses, and 11 administrative personnel serving 10 000 patients in a rural area near Otta wa, Ont. PARTICIPANTS Patients 50 years of age and older at risk of experiencing adverse health outcomes (N = 241). INTERVENTIONS At-risk patients were randomly assigned to receive usual care from their family physicians or Anticipatory and Preventive T eam Care (APTCare) from a collaborative team composed of their physicians, 1 of 3 nurse practitioners, and a pharmacist. MAIN OUTCOME MEASURES Quality of care for chronic disease management (CDM) for diabetes, coronary artery disease, congestive heart failure, and chronic obstructive pulmonary disease. RESULTS Controlling for baseline demographic characteristics, the APTCare approach impro ved CDM QOC by 9.2% (P < .001) compared with traditional care. The APTCare intervention also impro ved preventive care by 16.5% (P < .001). W e did not observe significant differences in other secondary outcome measures (intermediate clinical outcomes, quality of life [Short-Form 36 and health-related quality of life scales], functional status [instrumental activities of daily living scale] and service usage). CONCLUSION Additional resources in the form of collaborative multidisciplinary care teams with intensive interventions in primary care can impro ve QOC for CDM in a population of older at-risk patients. The appropriateness of this intervention will depend on its cost-effectiveness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".