Tools for Analyzing and Measuring the Performance of Care and Service Pathways
Bibliographic record
Abstract
Abstract Over the past thirty years, numerous calls have been made to promote the integration of health care and services in order to control dysfunctions in health care systems. The implementation of care and service pathways has been identified as an appropriate strategy to remedy these dysfunctions. In 2015, the government of Quebec (Canada) undertook a vast reorganization of the health and social services network, leading to significant changes in the governance of health care organizations. In this context, several organizations became interested in the principle of management by care and service pathways. To support teams aiming to implement this approach, tools were developed based on the expertise of a research team, a review of scientific and grey literature, consultation with an advisory committee, and practical experimentation with a pathway management organization. This chapter presents the components and functioning of the performance analysis model that was developed and proposes an example of the performance analysis carried out. This tool has the potential to support the teams involved in achieving better demographic and clinical outcomes, a better user experience and judicious use of resources.
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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 | 0.022 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".