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Record W2947908329 · doi:10.1186/s40814-019-0447-0

Supporting Harm Reduction through Peer Support (SHARPS): testing the feasibility and acceptability of a peer-delivered, relational intervention for people with problem substance use who are homeless, to improve health outcomes, quality of life and social functioning and reduce harms: study protocol

2019· article· en· W2947908329 on OpenAlexaff
Tessa Parkes, Catriona Matheson, Hannah Carver, John Budd, Dave Liddell, Jason Wallace, Bernie Pauly, Maria Fotopoulou, Adam Burley, Isobel Anderson, Graeme MacLennan, Rebecca Foster

Bibliographic record

VenuePilot and Feasibility Studies · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Victoria
FundersHealth Technology Assessment ProgrammeNational Institute for Health and Care Research
KeywordsIntervention (counseling)Harm reductionHarmPsychologyQuality (philosophy)Substance usePeer supportMedicineNursingClinical psychologyPsychiatrySocial psychologyPublic health

Abstract

fetched live from OpenAlex

BACKGROUND: While people who are homeless often experience poor mental and physical health and problem substance use, getting access to appropriate services can be challenging. The development of trusting relationships with non-judgemental staff can facilitate initial and sustained engagement with health and wider support services. Peer-delivered approaches seem to have particular promise, but there is limited evidence regarding peer interventions that are both acceptable to, and effective for, people who are homeless and using drugs and/or alcohol. In the proposed study, we will develop and test the use of a peer-to-peer relational intervention with people experiencing homelessness. Drawing on the concept of psychologically informed environments, it will focus on building trusting and supportive relationships and providing practical elements of support such as access to primary care, treatment and housing options. METHODS: A mixed-method feasibility study with concurrent process evaluation will be conducted to explore the feasibility and acceptability of a peer-delivered, relational intervention for people with problem substance use who are homeless. Peer Navigators will be based in homelessness outreach and residential services in Scotland and England. Peer Navigators will work with a small number of participants for up to 12 months providing both practical and emotional support. The sample size for the intervention is 60. Those receiving the intervention must be currently homeless or at risk of homelessness, over the age of 18 years and self-report alcohol/drug problems. A holistic health check will be conducted in the first few months of the intervention and repeated towards the end. Health checks will be conducted by a researcher in the service where the Peer Navigator is based. Semi-structured qualitative interviews with intervention participants and staff in both intervention and standard care settings, and all Peer Navigators, will be conducted to explore their experiences with the intervention. Non-participant observation will be conducted in intervention and standard care sites to document similarities and differences between care pathways. DISCUSSION: The SHARPS study will provide evidence regarding whether a peer-delivered harm reduction intervention is feasible and acceptable to people experiencing homelessness and problem substance use in order to develop a definitive trial. TRIAL REGISTRATION: SRCTN registry ISRCTN15900054, protocol version 1.3, March 12, 2018.

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)0.016
metaresearch head score (Gemma)0.017
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 score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.017
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0040.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0250.003

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 head0.392
GPT teacher head0.517
Teacher spread0.125 · 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

Citations27
Published2019
Admission routes1
Has abstractyes

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