Predictors of poor quality of life for patients discharged from acute psychiatric care in Alberta
Bibliographic record
Abstract
Background: Quality of life (Qol) is a multi-dimensional concept composed of various dimensions, including mental and/or psychological well-being and physical and/or biological health. Objective: The objective of this study is to assess the prevalence and predictors of poor Qol outcomes across the five dimensions of the EQ-5D-5L, namely, mobility, self-care, usual activities, pain/discomfort, and depression/anxiety for individuals discharged from acute psychiatric care in Alberta. Methods: Multiple binary logistic regression models were performed to examine the association between sociodemographic variables and EQ-5D-5L dimensions. Results: Out of the 1106 participants, the majority were Caucasian, 61.6%, 25 years or less, 36.4%, females 54.8%, and had a high school diploma, 51.4%. The prevalence of depression/anxiety in the cohort is 89.2%. Caucasians were two times more likely to present with problems regarding pain/discomfort (OR=2.14; 95% C.I. 1.39 - 3.27) compared to Black participants. Also, retired participants were three times more likely to present with pain/discomfort (OR 3.18; 95% C.I. = 1.45 - 6.96) than those employed. Finally, participants with likely anxiety were almost two times more likely to present problems relating to self-care (OR=1.99; 95% C.I. = 1.41 - 2.81) compared with those who had unlikely anxiety. Conclusion: This study's results highlight the complex interplay of demographic, socioeconomic, and mental health factors that influence various health-related problems. These findings underscore the importance of targeted, holistic health interventions that address physical and mental health needs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".