Systematic approach to evaluating and confirming the utility of a suite of national health system performance (HSP) indicators in Canada: a modified Delphi study
Bibliographic record
Abstract
OBJECTIVES: Evaluating an existing suite of health system performance (HSP) indicators for continued reporting using a systematic criteria-based assessment and national consensus conference. DESIGN: Modified Delphi approach with technical and leadership groups, an online survey of stakeholders and convening a national consensus conference. SETTING: A national health information steward, the Canadian Institute for Health Information (CIHI). PARTICIPANTS: A total of 73 participants, comprised 61 conference attendants/stakeholders from across Canada and 12 national health information steward staff. PRIMARY AND SECONDARY OUTCOME MEASURES: Indicator dispositions of retention, additional stakeholder consultation, further redevelopment or retirement. RESULTS: 4 dimensions (usability, importance, scientific soundness and feasibility) typically used to select measures for reporting were expanded to 18 criteria grouped under the 4 dimensions through a process of research and testing. Definitions for each criterion were developed and piloted. Once the definitions were established, 56 of CIHI's publicly reported HSP indicators were evaluated against the criteria using modified Delphi approaches. Of the 56 HSP indicators evaluated, 9 measures were ratified for retirement, 7 were identified for additional consultation and 3 for further research and development. A pre-Consensus Conference survey soliciting feedback from stakeholders on indicator recommendations received 48 responses (response rate of 79%). CONCLUSIONS: A systematic evaluation of HSP indicators informed the development of objective recommendations for continued reporting. The evaluation was a fruitful exercise to identify technical considerations for calculating indicators, furthering our understanding of how measures are used by stakeholders, as well as harmonising actions that could be taken to ensure relevancy, reduce indicator chaos and build consensus with stakeholders.
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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 | 0.293 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".