A more equitable approach to economic evaluation: Directly developing conceptual capability wellbeing attributes for Tanzania and Malawi
Bibliographic record
Abstract
Capability wellbeing can potentially provide a holistic outcome for health economic evaluation and the capability approach seems promising for African countries. As yet there is no work that has explored the evaluative space needed for health and care decision making at the whole population level and procedures that merely translate existing measures developed in the global north to contexts in the global south risk embedding structural inequalities. This work seeks to elicit the concepts within the capability wellbeing evaluative space for general adult populations in Tanzania and Malawi. Semi-structured interviews with 68 participants across Tanzania and Malawi were conducted between October 2021 and July 2022. Analysis used thematic coding frames and the writing of analytic accounts. Interview schedules were common across the two country settings, however data collection and analysis were conducted independently by two separate teams and only brought together once it was clear that the data from the two countries was sufficiently aligned for a single analysis. Eight common attributes of capability wellbeing were found across the two countries: financial security; basic needs; achievement and personal development; attachment, love and friendship; participation in community activities; faith and spirituality; health; making decisions without unwanted interference. These attributes can be used to generate outcome measures for use in economic evaluations comparing alternative health interventions. By centring the voices of Tanzanians and Malawians in the construction of attributes that describe a good life, the research can facilitate greater equity within economic evaluations across different global settings.
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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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".