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Record W4408263039 · doi:10.29173/pathfinder112

Health Literacy and the Opioid Crisis

2025· article· en· W4408263039 on OpenAlexaffvenue
Alison Brierley

Bibliographic record

VenuePathfinder A Canadian Journal for Information Science Students and Early Career Professionals · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOpioidHealth literacyOpioid epidemicLiteracyMedicinePsychologyPolitical scienceHealth careInternal medicinePedagogy

Abstract

fetched live from OpenAlex

As public health challenges, information sources, and research technologies change – so must the strategies that health librarians employ to make sure they are meeting the demands of their community. In recent years, one of the most difficult public health challenges to navigate as health information professionals is the opioid crisis. This literature review provides a summary and analysis on the impact of health literacy and health literacy interventions on the opioid crisis. It concludes that low levels of health literacy are linked to higher levels of opioid misuse, opioid dependency, and opioid misinformation and emphasizes the importance of health literacy interventions to improve outcomes. Opportunities for health sciences librarians to implement interventions and increase literacy are plentiful and include strategies such as plain language resource creation, community-led services, and collaboration between public, academic, and medical library environments.

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)0.003
metaresearch head score (Gemma)0.014
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: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.371
Teacher spread0.342 · 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
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 routes2
Has abstractyes

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