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Record W4390607355 · doi:10.1123/kr.2023-0020

Competencies Relevant to Physical Activity Specialists in Navigating Mental Health Contexts: A Scoping Review

2024· review· en· W4390607355 on OpenAlexaff
Ashley McCurdy, Yeong-Bae Kim, Carminda Goersch Lamboglia, Cliff Lindeman, Amie Mangan, Guy Faulkner, Wendy M. Rodgers, John C. Spence

Bibliographic record

VenueKinesiology Review · 2024
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of British ColumbiaUniversity of Northern British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsMental healthCINAHLPsycINFOPsychologyCoachingApplied psychologyWarrantConsistency (knowledge bases)Medical educationMEDLINENursingMedicinePsychological interventionPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

To inform future learning opportunities, we performed a scoping review to identify competencies relevant to physical activity (PA) specialists in supporting the PA and mental health of people experiencing mental health concerns. CINAHL, PsycINFO, and SPORTDiscus databases were searched up to June 22, 2022, for research studies and commentaries. Pertinent text was extracted and subject to content analysis using an inductive approach. Sixty-two competencies from 62 publications were organized into four domains: (a) interacting with mental health care services/systems, (b) responding to mental health concerns, (c) employing PA counseling/coaching to promote mental health among people with diverse mental health needs, and (d) building relationships that are responsive to diverse mental health needs. These findings may serve as a road map for stakeholders interested in developing PA specialists’ confidence to meet the challenges of navigating mental health contexts. Despite consistency across sources, points of divergence warrant consideration from learning institutions and professional bodies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.458
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.001
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.005

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 head0.612
GPT teacher head0.729
Teacher spread0.117 · 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 teacher head, not a consensus.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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