Incorporating equity issues into the development of Colombian clinical practice guidelines: suggestions for the GRADE approach
Bibliographic record
Abstract
Objective To propose how to incorporate equity issues, using the GRADE approach, into the development and implementation of Colombian Clinical Practice Guidelines. Methodology This proposal was developed in four phases: 1. Included a literature review and the development of a preliminary proposal about how to include equity issues; 2. Involved an informal discussion to reach a consensus on improving the first proposal; 3. Was a survey of the researchers' acceptance levels of the proposal, and; 4. A final informal consensus was formed to adjust the proposal. Results A proposal on how to incorporate equity issues into the GRADE approach was developed. It places particular emphasis on the recognition of disadvantaged populations in the development and implementation of the suggested guideline. PROGRESS-Plus is recommended for use in exploring the various categories of disadvantaged people. The proposal suggests that evidence be rated differentially by giving higher ratings to studies that consider equity issues than those that do not. The proposal also suggests the inclusion of indicators to monitor the impacts of the implementation of CPGs on disadvantaged people. Conclusions A consideration of equity in the development and implementation of clinical practice guidelines and quality assessments of the evidence would achieve more in the participation of potential actors in the process and reflect on the effectiveness of the proposed interventions across all social groups.
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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 | 0.392 | 0.595 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".