The prevalence and correlates of low resilience in patients prior to discharge from acute psychiatric units in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: 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. Low levels of resilience are under-explored, and this study investigates 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 levels of resilience where a score of less than 3.0 was indicative of low resilience. A chi-square analysis followed by a binary logistic regression model was employed to identify significant predictors of low resilience. RESULTS: A total of 1,004 individuals took part in this study. Of these 35.9% were less than 25 years old, 34.7% were above 40 years old, 54.8% were female, and 62.3% self-identified as Caucasian. The prevalence of low resilience in the study cohort was 55.3%. Respondents who identified as females 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 males. 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 (OR = 2.21; 95% C.I. = 1.45-3.70) or Asian (OR = 1.589; 95% C.I. = 1.45-2.44). Additionally, individuals with a diagnosis of depression were significantly more likely to have low resilience than those with a diagnosis of either bipolar disorder (OR = 2.567; 95% C.I. = 1.72-3.85) or schizophrenia (OR = 4.081;95% C.I. = 2.63-6.25). CONCLUSION: 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. CLINICAL TRIAL REGISTRATION: clinicaltrials.gov, NCT05133726. Registered on the 24th of November 2021.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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".