An investigation of the construct validity of the ICECAP-A capability measure
Bibliographic record
Abstract
PURPOSE: To investigate the construct validity of the ICECAP-A capability wellbeing measure. METHODS: A face-to-face interview-administered survey was conducted with 418 members of the UK general population, randomly sampled from the Postcode Address File. Pre-specified hypotheses were developed about the expected associations between individuals' ICECAP-A responses and their socio-economic circumstances, health and freedom. The hypotheses were investigated using statistical tests of association. RESULTS: The ICECAP-A responses and scores reflected differences across different health and socioeconomic groups as anticipated, but did not distinguish individuals by the level of local deprivation. Mean ICECAP-A scores reflected individuals' perceived freedom slightly more closely than did measures of health and happiness. CONCLUSION: This study suggests that the ICECAP-A measure can identify expected differences in capability wellbeing in a general population sample. Further work could establish whether self-reported capabilities exhibit desirable validity and acceptability in sub-groups of the population such as patients, social care recipients and informal carers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".