Work Productivity and Quality-of-Life of Mental Health Patients Attending Neuropsychiatric Hospital, Aro.
Bibliographic record
Abstract
INTRODUCTION: Improving mental health patients’ lost work productivity (LWP) may improve their health-related quality of life (HRQOL), and thus reduce their risk for more morbidity and mortality. METHODS: The study investigated the association between the LWP and HRQOL of 284 mental health follow-up patients at a neuro-psychiatric hospital in Nigeria. It was cross-sectional in design with data obtained quantitatively and analysed using the IBM SPSS version 20 at a significance level of p<0.05. RESULTS: The higher the LWP scores, the worse their level of work productivity but the higher the HRQOL scores, the better their HRQOL. There was a significant relationship between the LWP and HRQOL as every unit improvement in a number of the LWP scales, showed a corresponding significant increase in a number of the patient’s HRQOL domains for patients with schizophrenia or bipolar affective disorder. However, patients with depression or mental and behavioural disorders showed no such relationship. CONCLUSIONS: The lost work productivity scales and health-related quality of life domains’ assessments can be used as monitoring tools by physicians to assess the level of improvement of their patients to treatment. Their roles as prognostic tools can be tested in further studies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".