Development of integrated care pathways: toward a care management system to meet the needs of frail and disabled community-dwelling older people
Bibliographic record
Abstract
INTRODUCTION: The home care and services provided to older adults with the same needs are often inadequate and highly varied. Integrated care pathways (ICPs) can resolve these issues. The aim of this study was to develop the content of ICPs to follow-up frail and disabled community-dwelling older people. THEORY AND METHOD: A RIGOROUS PROCESS WAS APPLIED ACCORDING TO A SERIES OF STEPS: identification of desirable characteristics and a theoretical framework; review of evidence-based practices and current practices; and determination of ICPs by an interdisciplinary task team. RESULTS: ICPs are intended to prevent specific problems, maximize independence, and promote successful aging. They are organized according to a dynamic process: (1) needs assessment and assessment of risk/protection factors; (2) data-collection summary and goals identification; (3) planning of interventions from a client-centered view; (4) coordination, delivery, and follow-up; and (5) identification of variances, as well as review and adjustment of plans. CONCLUSION: Once computerized, these ICPs will facilitate the exchange of information as well as the clinical decision-making process with a perspective to adequately matching the needs of an individual person with resources that delay or slow the progression of frailty and disability. Once aggregated, the data will also support managers in organizing teamwork and follow-up for clients.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".