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Record W2108184144

An examination of outdoor experience in the development of character in young adults with Type 1 Diabetes

2012· article· en· W2108184144 on OpenAlexaboutno aff
Jennifer Hanson

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2012
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsnot available
Fundersnot available
KeywordsType 2 diabetesExperiential learningType 1 diabetesPopulationYoung adultPsychologyDiabetes mellitusGerontologyMedicinePedagogyEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

Connected in Motion is a not for profit organization serving young adults with Type 1 diabetes. The organization hosted outdoor and experiential Type 1 diabetes education programs in January of2009 and 2010. The weekends provided non-clinical alternative Type 1 diabetes education to the underserved population of young adults within Canada. Six women living with Type I diabetes and between the ages of 22 and 30 participated in the Winter Slipstream weekends participated in this phenomenological research study. Through semi-structured interviews and artifact-elicitation interviews, the lived experiences of the participants were examined. Data analysis indicated that the sense of community created through outdoor programming and experiential education for young adults with Type I diabetes stimulated the development of self-efficacy and participant-perceived improvement in Type 1 diabetes self-management. There was no indication that outdoor and experiential Type I diabetes education had any impact on the development of autonomy among participants. Recommendations are made to encourage the successful implementation of further alternative (non-clinical) Type 1

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)0.003
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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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