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Record W2198791440 · doi:10.12927/hcpol.2015.24365

Barriers to the Adoption of Safety-Engineered Needles Following a Regulatory Standard: Lessons Learned from Three Acute Care Hospitals

2015· article· en· W2198791440 on OpenAlexaffvenueabout
Andrea Chambers, Cameron Mustard, D. Linn Holness, Kathryn Nichol, F. Curtis Breslin

Bibliographic record

VenueHealthcare policy · 2015
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsUniversity Health NetworkPublic Health OntarioSt. Michael's HospitalInstitute for Work & Health
Fundersnot available
KeywordsHealth careAcute careNeedlestick injuryWork (physics)MedicineBusinessNursingPatient safetyOccupational safety and healthProduct (mathematics)Medical emergencyPublic relationsFamily medicinePolitical scienceEngineeringHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

BACKGROUND: A number of jurisdictions have introduced regulation to accelerate the adoption of safety-engineered needles (SENs). This study examined the transition to SENs in three acute care hospitals prior to and following the implementation of a regulatory standard in Ontario. This paper focuses on the ongoing barriers to the prevention of needlestick injuries among healthcare workers. METHODS: Information from document review and 30 informant interviews were used to prepare three case studies detailing each organization's implementation activities and outcomes. RESULTS: All three hospitals responded to the regulatory requirements with integrity and needlestick injuries declined. However, needlestick injuries continued to occur during the activation of safety devices, during procedures and during instrument disposal. The study documented substantial barriers to further progress in needlestick injury prevention. CONCLUSIONS: Healthcare organizations should focus on understanding their site-specific challenges that contribute to ongoing injury risk to better understand issues related to product limitations, practice constraints and the work environment.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.368
Teacher spread0.320 · 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.

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

Citations8
Published2015
Admission routes3
Has abstractyes

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