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Record W3138852835 · doi:10.1177/0008417421994385

Participation in Community Gardening: Sowing the Seeds of Well-Being: Participation au jardinage communautaire : pour semer les graines du bien-être

2021· article· en· W3138852835 on OpenAlexfundvenueno aff
Melinda Suto, Shelagh A. Smith, Natasha Damiano, Shurli Channe

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
FundersVancouver Foundation
KeywordsSowingHorticultureGeographyBiology

Abstract

fetched live from OpenAlex

BACKGROUND.: Sustaining well-being challenges people with serious mental health issues. Community gardening is an occupation used to promote clients' well-being, yet there is limited evidence to support this intervention. PURPOSE.: This paper examines how facilitated community gardening programs changed the subjective well-being and social connectedness of people living with mental health issues. METHOD.: A community-based participatory research approach and qualitative methods were used with 23 adults living in supported housing and participating in supported community gardening programs. A constructivist approach guided inductive data analysis. FINDINGS.: Participation in community gardening programs enhanced well-being through welcoming places, a sense of belonging, and developing positive feelings through doing. The connection to living things and responsibility for plants grounded participants in the present and offered a unique venue for learning about gardening and themselves. IMPLICATIONS.: Practitioners and service-users should collaborate to develop leadership, programs, places, and processes within community gardens to enhance well-being.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.074
GPT teacher head0.311
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations28
Published2021
Admission routes2
Has abstractyes

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