Does Meeting Needs Improve Quality of Life?
Bibliographic record
Abstract
BACKGROUND: This study investigated the relationship between patient-rated unmet needs and subjective quality of life using routine outcome data. METHODS: 265 mental health service patients from South Verona were assessed using the Camberwell Assessment of Need, the Lancashire Quality of Life Profile, and other standardised assessments of symptoms, disability, function and service satisfaction. At 1-year follow-up, 166 patients were still in contact, of whom 121 patients (73%) were re-assessed. RESULTS: Higher baseline quality of life was associated with being male, a diagnosis of psychosis, higher disability, higher satisfaction with care, fewer staff-rated or patient-rated unmet needs, and fewer patient-rated met needs (accounting for 40% of the variance). Specifically, fewer baseline patient-rated unmet needs were cross-sectionally associated with a higher quality of life (B = -0.08, 95% CI -0.12 to -0.04). Apart from its baseline value, the only baseline predictor of follow-up QoL was patient-rated unmet need (B = -0.08, 95% CI -0.21 to -0.09), accounting for 58% of the variance in follow-up quality of life. Graphical chain modelling confirmed this association. CONCLUSIONS: The association between high numbers of unmet needs and low subjective quality of life appears increasingly robust across several studies. Future research will need to investigate whether changes in needs precede changes in quality of life. This study provides further evidence that a policy of actively assessing and addressing patient-rated unmet needs may lead to improved quality of life.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".