The International Family Quality of Life Project: Goals and Description of a Survey Tool
Bibliographic record
Abstract
Abstract The International Family Quality of Life Project, begun in 1997, involves the collaboration of a team of researchers from Australia, Canada, Israel, and the United States whose aim was to conceptualize “family quality of life” and develop a survey tool. The authors describe the basis for the conceptualization and explain the survey development process. An initial version of the survey (the Family Quality of Life Survey—FQoLS‐2000) was used to collect FQoL data across several countries in the early 2000s. The experiences of survey respondents and administrators and subsequent data analysis suggested modifications that resulted in an updated version—the FQoLS‐2006. This new version focuses on 9 areas of family life: health, finances, family relationships, support from other people, support from disability‐related services, influence of values, careers and planning for careers, leisure and recreation, and community interaction. The authors explore each of these areas in relation to 6 underlying concepts: importance, opportunities, initiative, attainment, stability, and satisfaction. Other sections entail obtaining information on the family make‐up, family member, or members, with intellectual disability, and an overall summary of FQoL. The authors note that information from the FQoLS‐2006 should be useful for a wide variety of purposes related to providing supports to individuals and families.
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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 | 0.053 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".