Assessing demographic and clinical determinants of resilience, personal recovery, and quality of life for psychiatric in-patients before hospital discharge
Bibliographic record
Abstract
Introduction Many patients with mental health and emotional problems often see the transition period in the community after hospital discharge as a test of their resilience and a threat to their recovery. Most often, some doubt their ability to cope with the everyday challenges that may confront them in the community. Objectives This paper assesses how demographic and clinical characteristics predicted resilience, personal recovery and quality of life. Methods Data were collected from psychiatric inpatients prior to their discharge into the community using the REDCap, an online survey platform. Resilience, personal recovery, and quality of life were assessed using the Brief Resilience Scale (BRS), Recovery Assessment Scale (RAS), and EQ-Visual Analogue Scale (EQ-VAS), respectively. One-way analysis of covariance between groups (ANCOVA) was conducted to compare the relationships between groups. The dependent variables comprised mean scores of BRS, RAS and EQ-VAS. Demographic and clinical variables such as age, gender, ethnicity, and mental health diagnosis groups were independent variables, and covariates comprised demographic/clinical factors such as gender, ethnicity, and mental health diagnosis Results The survey results indicate that males had significantly higher resilience scores compared to females ( Mdiff = 0.270, CI= 0.144– 0.397, p=.<.001) and others (Mdiff =0.470, 0.093- 0.846, p=<.001); Black people indicated significantly higher quality of life than Caucasians (Mdiff = 8.79, 2.73- 14.85, P= <.001), and Indigenous people (Mdiff = 14.50, 6.45 - 22.51, p=<.001), respectively. In terms of relative recovery, participants with depression had significantly lower recovery compared to those with bipolar disorder (Mdiff = -10.25, -14.40- -6.10, p=<.001), schizophrenia (Mdiff f = -8.60, -13.20- -3.99, p=<.001), and substance use disorder (Mdiff = -8.30, -15.50- -1.42, p=<.005). Conclusions The present results indicate that women, younger adults, and Indigenous peoples may be more challenged in adapting to the challenges of post-discharge life in the community. Our data may be helpful in communicating to policymakers and providers of funds the need to implement and evaluate outcomes of inpatient and community programs focusing on supporting resilience to improve recovery outcomes after discharge from the patient setting. Disclosure of Interest None Declared
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".