The family quality of life survey-2006: an examination of relationships between objective and subjective data
Bibliographic record
Abstract
BACKGROUND: Family quality of life (FQOL) includes objective and subjective factors representing wellbeing for families of individuals with intellectual and developmental disabilities. The current project investigated the integration of objective and subjective data using the Family Quality of Life Survey - 2006 (FQOLS-2006). METHOD: 169 parents/caregivers of individuals with intellectual and developmental disabilities participated. Relationships were examined between objective information from the A Sections of the Health, Finances, and Support from Services domains of the FQOLS-2006, and more subjective FQOL ratings in the B Sections. RESULTS: We found strong relationships between the objective and subjective sections of the FQOLS-2006. Respondents reporting challenges in the A Sections generally reported lower Attainment/Satisfaction ratings in the B sections. However, some responses were discrepant. CONCLUSION: Objective and subjective measurements of FQOL were consistent but independent. Each type of measurement provided unique information and FQOL is best understood through the integration of these methods.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".