Evaluating the Prevalence and Correlates of Low Resilience in Patients Before Discharge from Acute Mental Health Units in Alberta, Canada
Bibliographic record
Abstract
Introduction Many people experience at least one traumatic event in their lifetime. Although such traumatic events can precipitate psychiatric disorders, many individuals exhibit high resilience by adapting to such events with little disruption or may recover their baseline level of functioning after a transient symptomatic period. Objectives To investigate the prevalence and correlates of low resilience in patients before discharge from psychiatric acute care facilities. Methods Respondents for this study were recruited from nine psychiatric in-patient units across Alberta. Demographic and clinical information were collected via a REDCap online survey. The brief resilience scale (BRS) was used to measure low resilience. A chi-square analysis followed by a binary logistic regression model was employed to identify significant predictors of low resilience. Results Overall, 1004 participants took part in this study; 360 (35.9%) were less than 25 years old, 269 (34.7%) were above 40 years old, and most participants were females 550 (54.8%) and Caucasians 625 (62.3%). The prevalence of low resilience in this cohort was (555/1004, 55.3%). Respondents who identified as female were one and a half times more likely to show low resilience (OR=1.564; 95% C.I.=1.79-2.10), while individuals with ‘other gender’ identity were three and a half times more likely to evidence low resilience (OR=3.646; 95% C.I.=1.36-9.71) compared to male gender persons. Similarly, Caucasians were two and one-and-a-half times respectively more likely to present with low resilience compared with respondents who identified as Black people (OR=2.21; 95% C.I.=1.45-3.70) and Asians (OR=1.589; 95% C.I.=1.45-2.44). Additionally, persons with a diagnosis of depression were more than two times and four times, respectively, more likely to present with low resilience than those with bipolar disorder (OR=2.567; 95% C.I.=1.72-3.85) and those with schizophrenia (OR=4.081;95% C.I.= 2.63-6.25) Conclusions Several demographic and clinical factors were identified as predictors of likely low resilience. The findings may facilitate the identification of vulnerable groups to enable their increased access to support programs that may enhance resilience. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".