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Record W4392912185 · doi:10.2196/54077

Implementation Documentation and Process Assessment of the PharmNet Intervention: Observational Report

2024· article· en· W4392912185 on OpenAlexvenueno aff
Lori Ann Eldridge, Beth E. Meyerson, Jon Agley

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
Keywords(+)-NaloxoneObservational studyOpioid overdoseDocumentationReferralIntervention (counseling)MedicineRandomized controlled trialPharmacyHarm reductionMedical emergencyMedical educationNursingPublic healthPublic relationsBusinessOpioidPolitical scienceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The number of overdose deaths in the United States involving opioids continues to exceed 100,000 per year. This has precipitated ongoing declarations of a public health emergency. Harm reduction approaches, such as promoting awareness of, ensuring access to, and fostering willingness to use naloxone to reverse opioid overdose, are a key component of a larger national strategy to address the crisis. In addition, overdose reversal with naloxone directly and immediately saves lives. Because of pharmacies' ubiquity and pharmacists' extensive clinical training, community pharmacies are well-positioned, in principle, to facilitate naloxone access and education. OBJECTIVE: In 2022, a single-site pilot study of PharmNet, a community pharmacy intervention incorporating naloxone distribution, awareness building, and referral, showed promising outcomes for both naloxone and resource distribution in the community. As a next step, this study was intended to be a pilot randomized controlled trial of PharmNet in 7 pharmacies. However, due to circumstances outside of the study team's control, data collection was unable to be fully completed as planned. In keeping with open research standards, we transparently report all available data from the study and discuss trial barriers and processes. We do so both to provide insights that may inform similar studies and to avoid the "file-drawer" (publication bias) problem, which can skew the aggregated scholarly literature through nonpublication of registered trial results or selective publication of findings affirming authors' hypotheses. METHODS: This paper reports an in-depth implementation study assessment, provides the available observational data, and discusses implementation considerations for similar studies in independent (eg, nonchain) community pharmacies. RESULTS: Retrospective assessment of study outcomes and fidelity data provided for robust discussion around how resource differences in independent community pharmacies (vs well-resourced chain pharmacies), as well as high demands on staff, can affect intervention implementation, even when leadership is highly supportive. CONCLUSIONS: Community pharmacies, particularly independent community pharmacies, may require more support than anticipated to be successful when implementing a new intervention into practice, even if it might affect estimates of real-world effectiveness. Further implementation science research is needed specific to independent community pharmacies. All study elements are outlined in the International Registered Report Identifier (IRRID) PRR1-10.2196/42373. Although this paper reports results associated with that registration, results and conclusions should not be given the weight assigned to findings from a preregistered study. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/42373.

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)0.053
metaresearch head score (Gemma)0.158
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.053
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.158
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.004
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.002

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 head0.105
GPT teacher head0.572
Teacher spread0.467 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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