Reported use of CBT techniques and their targets among Australian mental health practitioners: a mixed methods study
Bibliographic record
Abstract
Background Mental health practitioners of cognitive and behaviour therapies (CBTs) have an ethical obligation to select evidence-based techniques. Little data are available on which techniques are used and whether technique use varies as a function of a therapist’s theoretical orientation.Aim The study explored the use of CBT techniques among a diverse sample of Australian mental health practitioners (MHPs). The focus was to assess the extent to which different CBT techniques are used in their practice.Method A mixed methods study was conducted with a large MHP sample who were taking part in a post-qualification CBT training course.Results Of the 2,086 MHPs, 41% (n = 864) reported using behaviourally focused techniques, and 49% (n = 1,032) reported using cognitively focused techniques. Self-described theoretical orientation could differentiate technique use. A diverse range of CBT techniques were reported by MHPs in the present study.Conclusions The different ways MHPs used techniques put a spotlight on how different targets may be a focus of the same technique.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".