Use of Qualitative Methods to Explore the Quality-of-Life Construct From a Consumer Perspective
Bibliographic record
Abstract
OBJECTIVE: This study explored the construct of quality of life from the perspective of adults diagnosed as having severe and persistent mental illness, such as schizophrenia. METHODS: Qualitative research strategies, specifically in-depth interviews (N=18) and focus groups (N=35), were used to collect data. Interviews and focus groups took place in hospitals, community clinics, community agencies, and clients' homes. A convenience, snowball sampling strategy was utilized. RESULTS: Analysis using the constant comparative method resulted in the identification of two dominant themes. These themes permeated the results, crossed all domains, influenced the linkages between domains, and clearly influenced how individuals frame their expectations regarding quality of life. The first theme was the presence of stigma and its effects on everyday life and future planning, and the second was the pervasive fear of the return of major positive symptoms of psychosis, such as hallucinations, delusions, and general loss of contact with reality. In addition, four quality-of-life domains were identified-the experience of illness, relationships, occupation, and sense of self. CONCLUSIONS: Many persons with mental illness simply wish for the basics in life-mental and physical health, supportive relationships, meaningful occupations, and a positive sense of self-believing that acquisition of these basics will lead to a more satisfactory quality of life. Ensuring that they are able to obtain the basics requires action on their part, by those who support them, by service providers that interact with them, and by a more accepting society.
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".