Identifying and describing feelings and psychological flexibility predict mental health in men with HIV
Bibliographic record
Abstract
Background: Difficulty identifying and describing feelings (DIDF) and psychological flexibility (PF) predict poor emotional adjustment.\nObjective: To examine the relationship between DIDF and PF and whether DIDF and low PF would put men undergoing cancer screening at risk for poor adjustment.\nDesign: Longitudinal self‐report survey.\nMethods: Two hundred and one HIV‐infected men who have sex with men participated in anal cancer screening at two time points over 14 weeks. Psychological flexibility was assessed by the Acceptance and Action Questionnaire II and DIDF by the Toronto Alexithymia Scale‐20. We also measured depression, anxiety, stress (DASS) and health‐related quality of life (QOL; SF‐12).\nResults: Both DIDF and PF were reliable predictors of mental health. When levels of baseline mental health were controlled, greater DIDF predicted increases in Time 2 depression, anxiety and stress and decreases in mental and physical QOL. The link between PF and mental health was entirely mediated by DIDF.\nConclusions: Being chronically low in PF could lead to greater DIDF and thereby worse mental health. Having more PF promotes the ability to identify and differentiate the nuances of pleasant and unpleasant emotions, which enhances an individual's mental health. Intentionally enhancing men's ability to identify and describe feelings or PF may assist them to better manage a range of difficult life experiences such as health screenings and other potentially threatening information.
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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 | 0.000 | 0.000 |
| 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) | 0.004 | 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".