MétaCan
Menu
Back to cohort
Record W1711069922

Using community-based research to explore common language and shared identity in the therapeutic recreation profession in British Columbia, Canada.

2013· article· en· W1711069922 on OpenAlexaffabout
Colleen Reid, Anna Landy, Paloma León

Bibliographic record

VenueTherapeutic Recreation Journal · 2013
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsDouglas College
Fundersnot available
KeywordsRecreationIdentity (music)Participatory action researchProfessionalizationPsychological resilienceSociologyHumanismPublic relationsCitizen journalismPsychologyMedical educationPedagogySocial psychologyMedicineSocial sciencePolitical scienceLawAnthropology
DOInot available

Abstract

fetched live from OpenAlex

To date, very little peer-reviewed research on the therapeutic recreation (TR) profession has emerged from British Columbia (BC), Canada. The TR Research Network, a group of researchers and recreation therapists (RTs), adopted a community-based research approach to investigate the current state of TR in BC and to better understand common language and shared identity of diverse RTs in BC. Eighty-four (84) on-line surveys were gathered using Survey Monkey. Closed- and open-ended responses were coded numerically and thematically with the development of descriptive code books. Findings suggest that the profession in BC describes TR as “therapy,” uses clinical language to describe their work, and identifies with both humanistic and individualistic values. Research recommendations include bringing greater consistency to the language of TR, viewing research as the collaborative generation of practice-based evidence, and applying a strengths-based perspective to the ongoing professionalization of the field.

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)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.006
Science and technology studies0.0180.006
Scholarly communication0.0050.002
Open science0.0020.006
Research integrity0.0010.002
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 head0.257
GPT teacher head0.446
Teacher spread0.189 · 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 designQualitative
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

Citations3
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueTherapeutic Recreation JournalSame topicRecreation, Leisure, Wilderness ManagementFrench-language works237,207