Context-based evidence-based decision-making: case study of evidence utilisation in the development of cancer screening policy
Bibliographic record
Abstract
This thesis examines the impact of the decision-making context on the determination of what constitutes evidence and how that evidence is utilised in the development of health policy. Four cases were studied where expert groups were tasked with developing cancer screening policy recommendations for either breast, cervical, colorectal or prostate cancer in Ontario, Canada. A conceptual framework for evidence-based health policy was developed based on a process model of utilisation, consisting of three stages, including the introduction, interpretation and application of evidence. The decision-making context was divided into both an internal (where decision is made) and an external (where decision is applied) context, to more clearly distinguish how context can impact on evidence utilisation. Qualitative methods were employed to determine the nature of the evidence, the key components of both the internal and external decision-making contexts and how evidence was utilised for each of the four cases studied. Analysis of the empirical data led to refinement of the conceptual framework to better explain how the decision-making context impacted on evidence utilisation. Rather than a single three-stage process model of evidence utilisation, the process could be better described by three distinct phases, addressing efficacy/effectiveness (can/does it work?), feasibility (should we do it here?) and implementation (how do we do it here?) issues. Evidence hierarchies and stakeholder representation were the two main methods employed by the expert groups to utilise evidence. Evidence hierarchies focused attention on the quality of evidence, and were most appropriately applied to efficacy/effectiveness issues where experimental evidence was more likely. Stakeholder representation did not provide a clear mechanism for assessing the quality of evidence but did allow greater consideration of external contextual factors, which facilitated the assessment of the generalisability of evidence, making stakeholder representation more appropriate for assessing feasibility and implementation issues. The development of health policy needs to recognise the distinct features of each phase and the relative strengths and weaknesses of the two methods of evidence utilisation. This will allow a broader conception of evidence and encourage more active assessment of the generalisability of evidence while maintaining an appropriate focus on the quality of evidence.
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 | 0.082 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".