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Record W4413060235 · doi:10.3390/systems13070605

Connecting the Dots: Applying Network Theories to Enhance Integrated Paramedic Care for People Who Use Drugs

2025· article· en· W4413060235 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSystems · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of TorontoUniversity of WindsorHumber PolytechnicQueen's UniversityBC Mental Health & Substance Use Services
Fundersnot available
KeywordsCentralityHarmHealth careHarm reductionActor–network theoryWork (physics)Public relationsKnowledge managementMedicinePsychologyNursingSociologyPolitical scienceComputer scienceSocial psychologyPublic healthEngineering

Abstract

fetched live from OpenAlex

The evolving role of paramedics presents a unique opportunity to enhance care for people who use drugs, a population disproportionately affected by systemic barriers and inequities. In fragmented healthcare systems, paramedics are well-positioned to improve access through initiatives such as social prescribing and harm reduction. This theory-driven commentary explores how Network Theory and Actor Network Theory provide valuable theoretical underpinnings to conceptualize and strengthen the integration of paramedics into care networks. By emphasizing the centrality of paramedics and their connections with both human and non-human actors, these theories illuminate the relational dynamics that influence effective care delivery. We argue that leveraging paramedics’ positionality can address gaps in system navigation, improve patient outcomes, and inform policy reforms. Future work should examine the roles of other key actors, strengthen paramedic advocacy, and identify strategies to overcome barriers to care for people who use drugs.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.071
GPT teacher head0.419
Teacher spread0.348 · 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