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Record W2606842684 · doi:10.1186/s12954-017-0145-2

Expanding conceptualizations of harm reduction: results from a qualitative community-based participatory research study with people who inject drugs

2017· article· en· W2606842684 on OpenAlexafffundabout
Lisa M. Boucher, Zack Marshall, Alana Martin, Katharine Larose-Hébert, Jessica Flynn, Christine Lalonde, Dave Pineau, James H. Bigelow, Tiffany Rose, Robert M. Chase, Robert D. Boyd, Mark Tyndall, Claire Kendall

Bibliographic record

VenueHarm Reduction Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsBruyèreUniversity of OttawaBC Centre for Disease ControlRegent Park Community Health CentreUniversité LavalUniversity of ManitobaOttawa HospitalUniversity of WaterlooÉlisabeth Bruyère Hospital
FundersCanadian Institutes of Health Research
KeywordsHarm reductionHealth psychologyQualitative researchSocial policyCommunity-based participatory researchParticipatory action researchHarmSocial workSociologyPublic healthPsychologyMedicineSocial psychologyPolitical scienceNursingSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: The perspectives of people who use drugs are critical in understanding why people choose to reduce harm in relation to drug use, what practices are considered or preferred in conceptualizations of harm reduction, and which environmental factors interfere with or support the use of harm reduction strategies. This study explores how people who inject drugs (PWID) think about harm reduction and considers the critical imperative of equity in health and social services delivery for this community. METHODS: This community-based participatory research study was conducted in a Canadian urban centre. Using a peer-based recruitment and interviewing strategy, semi-structured qualitative interviews were conducted by and with PWID. The Vidaview Life Story Board, an innovative tool where interviewers and participant co-construct a visual "life-scape" using a board, markers, and customized picture magnets, was used to facilitate the interviews. The topics explored included injection drug use and harm reduction histories, facilitators and barriers to using harm reduction strategies, and suggestions for improving services and supports. RESULTS: Twenty-three interviews with PWID (14 men and 9 women) were analysed, with a median age of 50. Results highlighted an expanded conceptualization of harm reduction from the perspectives of PWID, including motivations for adopting harm reduction strategies and a description of harm reduction practices that went beyond conventional health-focused concerns. The most common personal practices that PWID used included working toward moderation, employing various cognitive strategies, and engaging in community activities. The importance of social or peer support and improving self-efficacy was also evident. Further, there was a call for less rigid eligibility criteria and procedures in health and social services, and the need to more adequately address the stigmatization of drug users. CONCLUSIONS: These findings demonstrated that PWID incorporate many personal harm reduction practices in their daily lives to improve their well-being, and these practices highlight the importance of agency, self-care, and community building. Health and social services are needed to better support these practices because the many socio-structural barriers this community faces often interfere with harm reduction efforts. Finally, "one size does not fit all" when it comes to harm reduction, and more personalized or de-medicalized conceptualizations are recommended.

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)0.078
metaresearch head score (Gemma)0.077
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 score0.078
Threshold uncertainty score0.411

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0190.023
Scholarly communication0.0070.007
Open science0.0040.014
Research integrity0.0030.005
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 head0.369
GPT teacher head0.514
Teacher spread0.145 · 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

Citations88
Published2017
Admission routes3
Has abstractyes

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