Coding Public Health Interventions for Health Technology Assessments: A Pilot Experience With WHO's International Classification of Health Interventions (ICHI)
Bibliographic record
Abstract
Introduction: An important requirement for successful public health interventions is a standardized classification in order to make these health technologies comparable in all contexts and recognized by all parties. The WHO International Classification of Health Interventions (ICHI), including an integrated public health component, has been developed to propose such an international standard. Methods: To test (a) the translation of public health interventions to ICHI codes and (b) the technical handling and general coding in public health, we used a set of public health interventions from a recent cross-sectional survey among Health Technology Assessment professionals. Results: Our study showed that handling of the ICHI interface is stable, that there is a need for specificity and adequate detail of intervention descriptions and desired outcomes to code adequately with ICHI and that the professional background of the coder, as well as his/her sex might influence the selection of codes. Conclusion: International Classification of Health Interventions provides a good coverage of public health interventions. However, the broader character of system wide interventions, often involving a variety of institutions and stakeholders, may present a challenge to the application of ICHI coding. Based on this experience, we would tailor future surveys more specifically to the needs of the classification and we advise training for health professionals before coding with ICHI. Standards of reporting will likely strengthen insights about the efficiency of primary prevention interventions and thus benefit long-term health of populations and structured HTA reporting process.
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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 | 0.358 | 0.464 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".