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Record W2010848235 · doi:10.1186/1472-6963-13-108

Enhancing screening, brief intervention, and referral to treatment among socioeconomically disadvantaged patients: study protocol for a knowledge exchange intervention involving patients and physicians

2013· article· en· W2010848235 on OpenAlexafffundabout
Ginetta Salvalaggio, Kathryn Dong, Christine Vandenberghe, Scott W. Kirkland, Kelsey Mramor, Taryn Brown, Marliss Taylor, Robert McKim, Greta G. Cummings, T. Cameron Wild

Bibliographic record

VenueBMC Health Services Research · 2013
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsZymeworks (Canada)University of TorontoRoyal Alexandra HospitalUniversity of Alberta
FundersAlberta InnovatesUniversity of AlbertaRoyal Alexandra Hospital FoundationAlberta Health Services
KeywordsMedicineBrief interventionReferralFamily medicineIntervention (counseling)Health carePublic healthDisadvantagedCommunity healthHealth informaticsNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Screening, Brief Intervention, and Referral for Treatment (SBIRT) is an effective approach for managing alcohol and other drug misuse in primary care; however, uptake into routine care has been limited. Uptake of SBIRT by healthcare providers may be particularly problematic for disadvantaged populations exhibiting alcohol and other drug problems, and requires creative approaches to enhance patient engagement. This knowledge translation project developed and evaluated a group of patient and health care provider resources designed to enhance the capacity of health care providers to use SBIRT and improve patient engagement with health care. METHODS/DESIGN: A nonrandomized, two-group, pre-post, quasi-experimental intervention design was used, with baseline, 6-, and 12-month follow-ups. Low income patients using alcohol and other drugs and who sought care in family medicine and emergency medicine settings in Edmonton, Canada, along with physicians providing care in these settings, were recruited. Patients and physicians were allocated to the intervention or control condition by geographic location of care. Intervention patients received a health care navigation booklet developed by inner city community members and also had access to an experienced community member for consultation on health service navigation. Intervention physicians had access to online educational modules, accompanying presentations, point of care resources, addiction medicine champions, and orientations to the inner city. Resource development was informed by a literature review, needs assessment, and iterative consultation with an advisory board and other content experts. Participants completed baseline and follow-up questionnaires (6 months for patients, 6 and 12 months for physicians) and administrative health service data were also retrieved for consenting patients. Control participants were provided access to all resources after follow-up data collection was completed. The primary outcome measure was patient satisfaction with care; secondary outcome measures included alcohol and drug use, health care and addiction treatment use, uptake of SBIRT strategies, and physician attitudes about addiction. DISCUSSION: Effective knowledge translation requires careful consideration of the intended knowledge recipient's context and needs. Knowledge translation in disadvantaged settings may be optimized by using a community-based participatory approach to resource development that takes into account relevant patient engagement issues. TRIAL REGISTRATION: Northern Alberta Clinical Trials and Research Centre #30094.

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)0.026
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.072
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.019
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0020.002
Science and technology studies0.0070.003
Scholarly communication0.0030.003
Open science0.0050.004
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0720.010

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 head0.075
GPT teacher head0.448
Teacher spread0.374 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations12
Published2013
Admission routes3
Has abstractyes

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