Resilience in individuals at clinical high risk for psychosis
Bibliographic record
Abstract
AIMS: It has been suggested that resilience may be a protective factor with respect to mental illness. This may be an important factor for those who are vulnerable to psychiatric illness. Thus, the aims of this paper were to compare levels of resilience between individuals at clinical high risk (CHR) for psychosis and healthy controls, and to examine associations between resilience and clinical measures, functioning and trauma of CHR participants. METHODS: Eighty participants, 40 CHR and 40 University of Calgary undergraduate students, completed two resilience questionnaires: the Connor-Davidson Resilience Scale and the Child and Youth Resilience Measure. RESULTS: A t-test revealed a significant difference between the groups on levels of resilience (t = 4.34, P < 0.01), demonstrating that CHR participants have lower levels of resilience than healthy controls. In terms of the associations between resilience and measures of mental health of CHR participants, it was found that higher levels of resilience were related to lower negative symptoms, depression and anxiety. Furthermore, resilient CHR participants showed higher levels of role functioning and generally reported higher positive schemas of self and others, as well as lower stress to reported life events. No associations were found between resilience and attenuated psychotic symptoms, social functioning, IQ and trauma. CONCLUSIONS: The results of the current study suggest that resilience may be beneficial to other mental issues present in CHR individuals but this may not be the case for attenuated psychotic symptoms.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".