Identify and classify interprofessional primary care performance indicators: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Measuring the performance of interprofessional primary care is needed to examine whether this model of care is achieving its desired outcomes on patient care and health system effectiveness as well as to guide quality improvement initiatives. The aim of this scoping review is to map the literature on primary care performance measurement indicators to determine the extent to which current indicators capture or could be adapted to capture processes, outputs and outcomes that reflect interprofessional primary care. METHODS AND ANALYSIS: The review will be guided by the six-stage framework by Arksey and O'Malley (2005). MEDLINE, Embase, CINAHL, grey literature and the reference list of key studies will be searched to identify any study, published in English or French between 2000 and 2022, related to the concepts of performance indicators, frameworks, interprofessional teams and primary care. Two reviewers will independently screen all abstracts and full-text studies for inclusion. Eligible indicators will be classified according to process, output and outcome domains proposed by two validated frameworks. This study started in November 2022 and is expected to be completed by July 2023. ETHICS AND DISSEMINATION: This review does not require ethical approval. The results will be disseminated through a peer-reviewed publication, conference presentations and presentations to stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.161 | 0.112 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.017 |
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".