Bibliographic record
Abstract
This paper reviews the meta-analysis of Brown, Bhimani and MacLeod and finds it an important contribution to the literature on performance reporting (PR) in healthcare. Of 1,053 papers identified in their search, only 62 met their criteria for inclusion in the review. The authors used a Knowledge, Attitudes and Behaviour (KAB) model for their analysis. Of the 63 papers reviewed, most related to individual consumers and groups of providers, fewer related to groups of consumers and individual providers. The principal findings were that there is a shortfall in the literature on PR to 2004 and limited evidence that PR is having any impact on KAB of consumers or providers. They appropriately conclude that there is a need for the development of a conceptual model and thereby the requirement that the goal of placing PR "within a context that focuses attention and that supports action" may be achieved. In general, the conclusions are defensible and, given the lack of impact of PR they have demonstrated, their work should stimulate response and action within healthcare systems. Finally, this paper concludes by proposing six characteristics of successful PR--clear definition of context and goals, objective and verifiable indicators, transparent and useable information, equitability in scope, engaging the continuum of care, and integrating measures of cost-utility.
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 | 0.322 | 0.590 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".