Designing and revising a cognitive behavioral group intervention for psychological distress among female university students
Bibliographic record
Abstract
Background: Psychological distress in the form of depression and/or anxiety has been found to be common among university students, especially in females. Roughly one in five of Icelandic female university students exhibit elevated psychological distress, yet less than 30% of them do receive professional mental health care. To ameliorate the psychological distress a cognitive behavioral group therapy was designed to target the distress. The purpose of this paper is to describe the main steps in designing the respective intervention and the revisions made by the expert panel based on the validation of the preliminary intervention and the experience of the advanced practice psychiatric nurses therapists.Methods: The intervention design took place in four phases. Initially psychological distress was defined, secondly a literature review was conducted to see if there were effective interventions available to solve the problem. Thirdly the drafting of the intervention took place based on theory and evidence and finally the intervention was validated with quantitative and qualitative methods. The intervention was provided by two advanced practice psychiatric nurses in 4 sessions in groups of 5 to 8 females. An expert panel of 6 psychiatric nurses was formed to guide the intervention design, the delivery of the intervention and intervention validation.Results: The quantitative and qualitative validation of the preliminary intervention showed that psychological distress decreased and was acceptable to participants.Conclusions: The validation of the preliminary intervention provided the expert group with rationale for modifying the content and structure of the intervention in nine categories.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".