Selecting contextually appropriate performance indicators in a circumpolar context: a modified Delphi approach
Bibliographic record
Abstract
BACKGROUND: Meaningful performance measurement requires indicators to be scientifically robust and strategically focused. For many circumpolar states, indicators aligned with national strategies may ignore the priorities of northern, remote, or Indigenous populations. The aim of this project was to identify contextually appropriate performance indicators for maternity care in circumpolar regions. METHODS: Fourteen maternity care and health systems experts participated in a modified Delphi consensus process. The list of proposed indicators was derived from a previously published scoping review. Fourteen participants rated each proposed indicator according to importance, circumpolar relevance, validity, and reliability and suggested additional indicators for consideration. RESULTS: Consensus was achieved after two rounds, as measured by a Cronbach's alpha of 0.87. Eleven indicators, many of which represented physical health outcomes, were ranked highly on all four criteria. Twenty-nine additional indicators, largely focused on social determinants of health, health care responsiveness, and accessibility, were identified for further research. Travel for care, cultural safety and upstream structural determinants of health were identified as important themes. CONCLUSIONS: This study identified the important gaps between current performance measurement strategies and the context and values that permeate maternal-child health in circumpolar regions. The indicators identified in this study provide an important foundation for ongoing work. We recommend that future work encompass an appreciation for the intersectoral nature of social, structural, and colonial determinants of maternal-child health in circumpolar regions.
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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 | 0.207 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".