SUPPORT Tools for evidence-informed health Policymaking (STP) 5: Using research evidence to frame options to address a problem
Bibliographic record
Abstract
This article is part of a series written for people responsible for making decisions about health policies and programmes and for those who support these decision makers. Policymakers and those supporting them may find themselves in one or more of the following three situations that will require them to characterise the costs and consequences of options to address a problem. These are: 1. A decision has already been taken and their role is to maximise the benefits of an option, minimise its harms, optimise the impacts achieved for the money spent, and (if there is substantial uncertainty about the likely costs and consequences of the option) to design a monitoring and evaluation plan, 2. A policymaking process is already underway and their role is to assess the options presented to them, or 3. A policymaking process has not yet begun and their role is therefore to identify options, characterise the costs and consequences of these options, and look for windows of opportunity in which to act. In situations like these, research evidence, particularly about benefits, harms, and costs, can help to inform whether an option can be considered viable. In this article, we suggest six questions that can be used to guide those involved in identifying policy and programme options to address a high-priority problem, and to characterise the costs and consequences of these options. These are: 1. Has an appropriate set of options been identified to address a problem? 2. What benefits are important to those who will be affected and which benefits are likely to be achieved with each option? 3. What harms are important to those who will be affected and which harms are likely to arise with each option? 4. What are the local costs of each option and is there local evidence about their cost-effectiveness? 5. What adaptations might be made to any given option and could they alter its benefits, harms and costs? 6. Which stakeholder views and experiences might influence an option's acceptability and its benefits, harms, and costs?
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".