Considering health equity when moving from evidence-based guideline recommendations to implementation: a case study from an upper-middle income country on the GRADE approach
Bibliographic record
Abstract
The availability of evidence-based guidelines does not ensure their implementation and use in clinical practice or policy making. Inequities in health have been defined as those inequalities within or between populations that are avoidable, unnecessary and also unjust and unfair. Evidence-based clinical practice and public health guidelines ('guidelines') can be used to target health inequities experienced by disadvantaged populations, although guidelines may unintentionally increase health inequities. For this reason, there is a need for evidence-based clinical practice and public health guidelines to intentionally target health inequities experienced by disadvantaged populations. Current guideline development processes do not include steps for planned implementation of equity-focused guidelines. This article describes nine steps that provide guidance for consideration of equity during guideline implementation. A critical appraisal of the literature followed by a process to build expert consensus was undertaken to define how to include consideration of equity issues during the specific GRADE guideline development process. Using a case study from Colombia we describe nine steps that were used to implement equity-focused GRADE recommendations: (1) identification of disadvantaged groups, (2) quantification of current health inequities, (3) development of equity-sensitive recommendations, (4) identification of key actors for implementation of equity-focused recommendations, (5) identification of barriers and facilitators to the implementation of equity-focused recommendations, (6) development of an equity strategy to be included in the implementation plan, (7) assessment of resources and incentives, (8) development of a communication strategy to support an equity focus and (9) development of monitoring and evaluation strategies. This case study can be used as model for implementing clinical practice guidelines, taking into account equity issues during guideline development and implementation.
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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 | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 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".