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Record W4315823144 · doi:10.53841/bpscpr.2011.26.2.57

Is it time we turn towards ‘third wave’ therapies to treat depression in primary care? A review of the theory and evidence with implications for counselling psychologists

2011· review· en· W4315823144 on OpenAlexaff
Salena Bhanji

Bibliographic record

VenueCounselling Psychology Review · 2011
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMindfulnessPsychotherapistThird wavePsychologyAcceptance and commitment therapyDepression (economics)Primary careEmpirical researchCognitionClinical psychologyMedicinePsychiatryIntervention (counseling)Family medicine

Abstract

fetched live from OpenAlex

Introduction The current Increasing Access to Psychological Therapies (IAPT) agenda that focuses on the widespread dissemination of CBT in primary care services is considered alongside the emergence of third wave therapies and their role in the treatment of depression. Review Theoretical models and empirical outcomes for Mindfulness-based Cognitive Therapy, Metacognitive Therapy, and Acceptance and Commitment Therapy as third wave treatments for depression are examined with implications for how mindfulness approaches can be integrated into the therapeutic work of counselling psychologists (CoPs). Conclusion Further theoretical and empirical considerations for the inclusion of third wave therapies into the IAPT agenda are discussed with suggestions for further research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.200
GPT teacher head0.456
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2011
Admission routes1
Has abstractyes

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