Exploring the identification, validation, and categorization of costs and benefits of education in mental health: The PECUNIA project
Bibliographic record
Abstract
Background Mental health problems can lead to costs and benefits in other sectors (e.g. in the education sector) in addition to the healthcare sector. These related costs and benefits are known as intersectoral costs and benefits (ICBs). Although some ICBs within the education sector have been identified previously, little is known about their extensiveness and transferability, which is crucial for their inclusion in health economics research. Objectives The aim of this study was to identify ICBs in the education sector, to validate the list of ICBs in a broader European context, and to categorize the ICBs using mental health as a case study. Methods Previously identified ICBs in the education sector were used as a basis for this study. Additional ICBs were extracted from peer-reviewed literature in PubMed and grey literature from six European countries. A comprehensive list of unique items was developed based on the identified ICBs. The list was validated by surveying an international group of educational experts. The survey results were used to finalize the list, which was categorized according to the care atom. Results Additional ICBs in the education sector were retrieved from ninety-six sources. Fourteen experts from six European countries assessed the list for completeness, clarity, and relevance. The final list contained twenty-four ICBs categorized into input, throughput, and output. Conclusion By providing a comprehensive list of ICBs in the education sector, this study laid further foundations for the inclusion of important societal costs in health economics research in the broader European context.
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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 | 0.054 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".