Evaluating complex transformation
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to present a detailed case study of the evaluation strategies of a complex, multi-faceted response to a public health emergency: drug-related overdose deaths. It sets out the challenges of evaluating such a complex response and how they were overcome. It provides a pragmatic example of the rationale and issues faced to address the what, the why and particularly the how of the evaluation. DESIGN/METHODOLOGY/APPROACH: The case study overviews British Columbia's Provincial Response to the Overdose Public Health Emergency, and the aims and scope of its evaluation. It then outlines the conceptual approach taken to the evaluation, setting out key methodological challenges in evaluating large-scale, multi-level, multisectoral change. FINDINGS: The evaluation is developmental and summative, utilization focused and system informed. Defining the scope of the evaluation required a strong level of engagement with government leads, grantees and other evaluation stakeholders. Mixed method evaluation will be used to capture the complex pattern of relationships that have informed the overdose response. Working alongside people with drug use experience to both plan and inform the evaluation is critical to its success. ORIGINALITY/VALUE: This case study builds on a growing literature on evaluating large-scale and complex service transformation, providing a practical example of this.
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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 | 0.084 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".