MétaCan
Menu
Back to cohort
Record W4400870396 · doi:10.1177/08404704241264243

On the pathway to health equity: Creating a harm reduction strategy for a large academic acute care hospital

2024· article· en· W4400870396 on OpenAlexaffabout
Heather Lokko, Wisdom Mensah Kwasi Avor

Bibliographic record

VenueHealthcare Management Forum · 2024
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsHarm reductionHarmHealth careEquity (law)NursingWork (physics)Acute carePublic relationsMedicineBusinessMedical emergencyPsychologyPublic healthPolitical science

Abstract

fetched live from OpenAlex

An urban centre in southwestern Ontario continues to be faced with the extensive impacts of high rates of substance use. To more effectively meet the Quintuple Aim, in a way that authentically considers patients who use substances, the need for a cohesive, comprehensive organizational harm reduction strategy at the large academic acute care hospital providing community and regional healthcare services was clear. Community-based harm reduction expertise was leveraged to support the development work. Information gathered through literature review and interviews with patients, internal staff and leaders, partner healthcare agencies, and Canadian hospitals leading in harm reduction work provided key insights and supported the formulation of emerging recommendations that will be used to shape the acute care hospital's formal organizational harm reduction strategy.

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)0.042
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0330.011
Scholarly communication0.0170.009
Open science0.0040.022
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0070.001

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 head0.038
GPT teacher head0.384
Teacher spread0.346 · 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 designQualitative
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 routes2
Has abstractyes

Explore more

Same venueHealthcare Management ForumSame topicOpioid Use Disorder TreatmentFrench-language works237,207