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Record W2319408593 · doi:10.3109/09687637.2016.1149147

Views of Iranian prison staff towards needle-exchange program in prison

2016· article· en· W2319408593 on OpenAlexaff
Mohammad Shahbazi, Babak Moazen, Farimah Rezaei, Mostafa Shokoohi, Marzieh Farnia, Ghobad Moradi, Kate Dolan

Bibliographic record

VenueDrugs Education Prevention and Policy · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsWestern University
Fundersnot available
KeywordsPrisonHarm reductionHarmCriminologyNursingPrison populationQualitative researchFocus groupPsychologyMedicineHealth careMental healthPsychiatryPolitical scienceSociologySocial psychologyPublic healthLaw

Abstract

fetched live from OpenAlex

Aims: Prison staff members have a core role in the provision of harm reduction strategies in prison. This study aimed to evaluate the attitudes of prison directors, managers and health staff toward Needle Exchange Program (NEP) among people who inject drugs in Iranian prisons. Methods: With a grounded theory design, this qualitative study was conducted in 2011 in Iran. The study population included directors of provincial prison organisations, prison managers, heads of health departments in prisons, prison health officers, physicians, counsellors and healthcare workers in prisons. Participants’ responses were collected via focus group discussions. Results: Attitudes of the participants could generally be categorised in three including: Health-related aspects; Behavioural and social aspects; and Legal, organisational and financial aspects. Those who were in line with the existence of NEP in prisons mentioned some pre-requisites for supporting this program in prisons. Conclusion: Positive and negative views of Iranian prison staff toward NEP in prisons suggest that there are many obstacles to the provision of harm reduction strategies in prison. Consideration of socio-cultural parameters of the target community as well as combination of NEP and other harm reduction strategies might help to improve the effectiveness of harm reduction in prisons.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.052
GPT teacher head0.417
Teacher spread0.365 · 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 designOther design
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
Published2016
Admission routes1
Has abstractyes

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