Opening the black box of health systems performance management using the behaviour change techniques taxonomy: implications for health research and practice
Bibliographic record
Abstract
BACKGROUND: Performance management (PM) systems in healthcare consist of many interacting interventions, such as contracts, scorecards and incentives. The diversity, complexity and poor description of PM interventions hampers replication in research, standardized comparative analysis and accumulation of evidence. Specifying PM systems and interventions in terms of their behaviour change techniques (BCTs) using standardized language can address these challenges and clarify the mechanisms linking system-level PM with individual behaviours. METHODS: We conducted an analysis of BCTs in a PM system in Ontario, Canada using a modified behaviour change technique taxonomy (BCTT). We reviewed 64 documents, observed 15 meetings and conducted 4 semi-structured interviews with key informants to map the PM interventions on to the taxonomy. RESULTS: We identified 54 BCTs spanning 13 taxonomy domains in the PM system. BCTs were concentrated in four domains: (1) goals and planning, (2) reward and threat, (3) feedback and monitoring and (4) identity. The BCTs coded most often included: (1) discrepancy between current behaviour and goal, (2) feedback on outcome(s) of behaviour, (3) social comparison and (4) social incentive/reward. These BCTs suggest that this PM system seeks to change behaviour primarily by directing programme attention to their current performance in relation to the target and in relation to other programs across the province, and by acknowledging good performance with praise or recognition. A total of five PM interventions accounted for 58% of identified BCTs - the scorecard, quarterly performance review reports, quarterly performance review meetings, escalation letter for poor or declining performance and the improvement action plan. CONCLUSIONS: The results provide a unique analytical and evaluative characterization of the PM system, revealing how a behaviour-change lens on health systems PM can support the (re)design, standardized comparison, and evaluation of PM systems in research and in practice.
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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 | 0.166 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| 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".