Performance measurement and improvement frameworks in health, education and social services systems: a systematic review
Bibliographic record
Abstract
PURPOSE: To perform a systematic review, supplemented by a targeted grey literature scan, for performance measurement and improvement frameworks within and across the health, education and social service systems. The intended outcome was the creation of a foundation of evidence to inform the development of cross-sectoral quality improvement frameworks. DATA SOURCES: MEDLINE, CINAHL, PsycINFO, ERIC, EMBASE, Social Services Abstracts, Social Work Abstracts and Education Index Full Text were searched up to April/May 2007. In addition, 26 governmental and 27 organizational websites were searched. STUDY SELECTION: English language material with a publication date of 1986 or more recent that described a health, education or social services multidimensional framework for performance measurement and improvement. Data extraction The framework name; administrative sector; level of application; setting; population of interest; categories of quality described within the framework; country of application; and citations to other performance measurement and improvement frameworks were extracted from each article. RESULTS: In total, 111 frameworks were identified. Most frameworks (n = 97) were developed in or for the health sector. A concept sorting exercise identified 16 quality concepts applicable across many settings, sectors and levels of application. CONCLUSION: This systematic review of quality domains will be relevant and useful to those who are developing and using performance measurement and improvement frameworks for adult and child populations within or across the health, social service or education sectors.
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 | 0.081 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".