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Record W3204983046 · doi:10.5539/mas.v15n6p19

Who Are You Without Your Substance? Transforming Occupational Time Use in Recovery

2021· article· en· W3204983046 on OpenAlexvenueno aff
Paula Jarrard, Sadie Cunningham, Paxton Granda, Paige Harker, Taylor Lannan, Kristine Price

Bibliographic record

VenueModern Applied Science · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyPsychologyIntervention (counseling)PopulationClinical psychologyMedicineNursingApplied psychologyPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Medically underserved rural communities struggle to meet challenging needs in response to the opioid crisis. The purpose of this study was to measure any benefit of an occupational therapy (OT) intervention group with participants in an addiction recovery program. Supervised OT graduate students implemented a five-week program at a faith-based non-profit organization in a small metro community. A weekly time management occupational-based intervention group based on the Action Over Inertia (AOI) manualized protocol focuses on motivating and providing strategies to successfully reintegrate individuals into the community by using meaningful activity and positively influencing levels of occupational balance and engagement (Krupa et al., 2003). Outcome measures included self-report of time use, occupational balance, occupational engagement, and goal identification, satisfaction, and performance. The need to effectively treat individuals with SUD is a public health priority. Results demonstrated positive outcomes with self-rating of time management, self-management skills, frequency of engaging in meaningful activities, and performance and satisfaction in meeting individual goals. This research adds to the limited evidence base in the OT literature for interdisciplinary treatment of this population using a manualized occupation-based intervention. 

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.151
GPT teacher head0.439
Teacher spread0.288 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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