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Record W4411298792 · doi:10.1080/09687637.2025.2517071

First responder attitudes and practices related to people who use drugs: exploring the impact of resource sharing

2025· article· en· W4411298792 on OpenAlexaff
Natasha S. Mendoza, Andrea N. Cimino, Lindsey Cantelme

Bibliographic record

VenueDrugs Education Prevention and Policy · 2025
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsRogue Research (Canada)
FundersArizona State University
KeywordsResource (disambiguation)Shared resourceFirst responderPsychologyMedicineBusinessMedical emergencyComputer securityComputer science

Abstract

fetched live from OpenAlex

Background First responders are critical to preventing recurrent opioid overdoses and improving overdose response. However, their attitudes and behaviors toward people who use drugs (PWUD) remain underexplored, limiting opportunities for effective intervention. This study examined first responders’ perceptions, focusing on their attitudes, crisis response behaviors, and resource-sharing practices.Methods This research is part of a larger, mixed-methods study that included pre- and post-test survey data from first responders (N = 38 and 30, respectively) piloting a mobile app designed to support overdose response and resource dissemination.Results After the intervention, first responders reported increased concern for users’ well-being and community health, along with a greater appreciation for the importance of sharing resources related to basic needs.Conclusion Training and tools that empower first responders to connect PWUD with harm reduction, treatment, and counseling resources can foster trust, enhance public health outcomes related to overdose, and reduce overdose risks in communities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.110
GPT teacher head0.486
Teacher spread0.376 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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