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Record W4391691214 · doi:10.1111/inm.13302

Barriers to using physical exercise as an intervention within inpatient mental health settings: A systematic review

2024· review· en· W4391691214 on OpenAlexaff
Catriona McKenna, Beata Moyo, John Goodwin

Bibliographic record

VenueInternational Journal of Mental Health Nursing · 2024
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMental healthPsychological interventionIntervention (counseling)Service providerNursingMental health serviceService (business)MedicineMEDLINEPsychologyPsychiatryBusiness

Abstract

fetched live from OpenAlex

Service providers find it difficult to implement Physical Exercise (PE) strategies in routine care within inpatient mental health settings even though they perceive it to be an effective therapy, with a robust evidence base. Identifying barriers that exist can assist with the development of future interventions and support PE services being introduced into mental health inpatient settings. The aim of this systematic review was to synthesise the evidence on the barriers or perceived barriers that exist amongst service users and providers when incorporating PE as an intervention within inpatient mental health settings. Using a narrative synthesis approach, four main themes were identified: (i) Barriers relating to service users' mental and physical health, (ii) Factors relating to service providers, (iii) Environmental factors and (iv) Cultural factors. Both service users and providers need more knowledge on implementing PE in inpatient mental health settings. Tailored programmes for service users are warranted, with specialist roles for staff developed.

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)0.016
metaresearch head score (Gemma)0.086
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 score0.016
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.086
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.050
GPT teacher head0.492
Teacher spread0.442 · 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

Citations20
Published2024
Admission routes1
Has abstractyes

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