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Record W2890150215 · doi:10.1080/01490400.2018.1483851

The Impacts of Recreation Programs on the Mental Health of Postsecondary Students in North America: An Integrative Review

2018· article· en· W2890150215 on OpenAlexaff
Fenton Litwiller, Catherine White, Barbara Hamilton-Hinch, Robert Gilbert

Bibliographic record

VenueLeisure Sciences · 2018
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsDalhousie UniversityUniversity of Manitoba
Fundersnot available
KeywordsRecreationMental healthInclusion (mineral)Psychological interventionPsychologyMeditationAnxietyMoodMindfulnessClinical psychologyGerontologyPsychotherapistSocial psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

The mental health of university students has become a recognized concern and lifestyle factors, including recreation, can play an integral role in maintaining positive mental health. The objective of this integrative review was to consolidate our understanding of the efficacy of post-secondary institution-based recreation programs developed with the purpose of supporting students’ mental health. Inclusion criteria included non-clinical populations of undergraduate students and North American studies (2005 to 2016) that used valid approaches to measure changes in mental health. Twenty-one studies met our inclusion criteria and were critically appraised. Results indicate recreation programs that emphasize Mindfulness, meditation, Tai Chi, yoga, exercise, and animal therapy may reduce perceived stress, anxiety, depression and negative mood. Future research should consider the effect of interventions on sex and gender categories and measure more mental health outcomes, in particular social outcomes, against a broader definition of recreation activities (e.g., arts).

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)0.002
metaresearch head score (Gemma)0.006
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.405
Teacher spread0.367 · 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

Citations57
Published2018
Admission routes1
Has abstractyes

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