Assessing the psychometric properties of a modified global wellbeing measure in Ghana
Bibliographic record
Abstract
We live in a world faced by unprecedented environmental change. As such, it is difficult to fathom how we might define, measure, and monitor related impacts on population wellbeing. This is especially the case in low to middle income countries that lack holistic national wellbeing measures but are the most impacted by global environmental changes. As part of a larger research program that aims to develop a Global Index of Wellbeing (GLOWING), this paper reports the assessment of the psychometric properties of a community wellbeing measure, using Ghana as a case study. Informed by the ecosocial and capabilities frameworks and in-depth qualitative and focus group discussions, survey data (n = 1036) were collected from three regions in Ghana to assess population wellbeing across several domains. Using structural equation modelling, psychometric properties of this modified wellbeing measure were tested to show the relative contribution of each domain to overall wellbeing. Pathways between domains and overall wellbeing were also investigated. The modified wellbeing measure showed good sensitivity, validity, and reliability which makes it suitable as a valuable tool for measuring wellbeing in Ghana and perhaps other LMIC settings. Furthermore, while the range of wellbeing constructs showed significance across the three regions studied, their relative importance differed, underscoring the importance of place to the measurement of wellbeing. Multivariate analysis shows a multiple range of factors [living conditions, sense of community, perception of environmental quality and political participation] were associated with wellbeing, requiring innovative, flexible and action-oriented approaches to improving population wellbeing. In this regard, we propose modifications to the scale to enable it to capture the role of place, while allowing for comparisons across space.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".