Resilience, personal recovery, and quality of life for psychiatric in-patients prior to hospital discharge: demographic and clinical determinants
Bibliographic record
Abstract
Introduction: Patients with mental health challenges often see the transition from hospital to community as a test of resilience and a potential threat to recovery. Many question their ability to cope with everyday challenges. This paper examines how demographic and clinical factors predict resilience, personal recovery, and quality of life. Methods: Data were collected from psychiatric inpatients before discharge using REDCap, an online survey platform. Resilience, recovery, and quality of life were assessed with the Brief Resilience Scale (BRS), Recovery Assessment Scale (RAS), and EQ-Visual Analogue Scale (EQ-VAS). ANCOVA was used to compare group relationships. Demographic and clinical variables such as age, gender, ethnicity, and mental health diagnosis were independent variables. Results: Males had significantly higher resilience scores than females (Mdiff = 0.270, p<.001) and others (Mdiff = 0.470, p<.001). Self-identified Black individuals had higher quality of life scores than Caucasians (Mdiff = 8.79, p<.001) and Indigenous individuals (Mdiff = 14.50, p<.001). Participants with depression had significantly lower recovery scores compared to those with bipolar disorder (Mdiff = -10.25, p<.001), schizophrenia (Mdiff = -8.60, p<.001), and substance use disorder (Mdiff = -8.30, p<.005). Conclusion: Results suggest that women, younger adults, Indigenous peoples, and individuals with depression struggle more with adapting to post-discharge life. Policymakers should implement programs that focus on supporting resilience in these vulnerable groups.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".