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Record W2969365014 · doi:10.1080/13561820.2019.1637334

Collaborative practice in counselling: a scoping review

2019· review· en· W2969365014 on OpenAlexaff
Elaine Greidanus, C Warren, Gregory E. Harris, Yayo Umetsubo

Bibliographic record

VenueJournal of Interprofessional Care · 2019
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of TorontoMemorial University of NewfoundlandUniversity of Lethbridge
Fundersnot available
KeywordsMedical educationPsychologyMedicineNursing

Abstract

fetched live from OpenAlex

Collaborative care (interdisciplinary/interprofessional teamwork) in mental health is emerging as best practice in primary care, hospitals, and government agencies. Counsellors have much to offer and benefit from working with other professions in service of their clients. While most health professions are well on their way integrating collaborating with one another in practice, it is yet unclear how often, and in what ways, counsellors are included in these teams. This scoping review of the literature on collaborative practice in counselling addresses the question: "What is the role of Professional Counselling and Clinical/Counselling Psychology in a collaborative model of mental health care?" This scoping review looks at 40 studies published between 2012 and 2015. Counsellors are often included on multidisciplinary teams in diverse roles. Specific collaborative activities are discussed along with ethical and educational implications.

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)0.018
metaresearch head score (Gemma)0.062
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0140.019
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0020.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.073
GPT teacher head0.577
Teacher spread0.504 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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