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Using A Harm Reduction Approach With Clients Who Have Alcohol/Drug Dependencies In A Spinal Cord Rehabilitation Program

2002· article· en· W2415562584 on OpenAlexaffabout
Jenny Young, William W. Fish, Alister Browne, Richard S. Lawrie

Bibliographic record

VenueJournal of Spinal Cord Medicine · 2002
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of British ColumbiaGF Strong Rehabilitation CentreLangara CollegeVancouver Hospital and Health Sciences Centre
Fundersnot available
KeywordsRehabilitationHarmHarm reductionPsychologyMedicinePhysical therapyNursingSocial psychologyPublic health

Abstract

fetched live from OpenAlex

BACKGROUND: Dissatisfied with the frequently adversarial nature of relationships with clients who use alcohol or drugs while rehabilitation inpatients, and the often less than optimal outcomes for these individuals, the Spinal Cord Program at the G.F. Strong Rehabilitation Center in Vancouver, BC, decided to pilot a new approach. OBJECTIVE: The goal of the pilot project is to promote successful rehabilitation, including less conflict in rehabilitation, a completed rehabilitation program, and continued connection after discharge if needed. METHOD: A dedicated team was formed and trained to work with these clients using harm reduction principles. PARTICIPANTS: From its inception in December 2000, through May 2001, the team worked with 6 inpatients, 12% of admissions to the Spinal Cord Program during that period. RESULTS: Outcomes based on the above goals have been positive. There have been no discharges against a client's will or instances of significant conflict with the team. Several clients have returned to the center for assistance or to visit post-discharge. Only 1 client left rehabilitation prematurely.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0120.002
Scholarly communication0.0020.001
Open science0.0030.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.113
GPT teacher head0.382
Teacher spread0.270 · 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 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

Citations5
Published2002
Admission routes2
Has abstractyes

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Same venueJournal of Spinal Cord MedicineSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207