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Record W6926513919 · doi:10.22038/psj.2025.85290.1457

The Hidden Costs of Convenience: Why Reusing Needles in Healthcare Remains Common Practice Despite Known Risks

2025· article· en· W6926513919 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSAFERReuseHealth carePublic healthControl (management)Public healthcarePatient safetyResource (disambiguation)

Abstract

fetched live from OpenAlex

The reuse of needles in healthcare settings persists despite well-documented risks, posing significant threats to patient safety and public health. Studies indicate that reusing needles increases the likelihood of contamination, transmission of bloodborne pathogens, and local complications. This practice is fueled by resource constraints, lack of awareness, systemic inefficiencies, and cultural normalization of unsafe practices. For example, reusing needles in procedures such as Botox injections or repeated IV insertions, while seemingly cost-effective, leads to higher long-term costs due to complications and the need for additional treatments. This paper reviews the risks associated with needle reuse, highlights the factors contributing to its persistence, and explores comprehensive strategies for mitigating these risks. Recommendations include enhancing education and training for healthcare workers, implementing safety-engineered devices, adhering strictly to infection control guidelines, improving infrastructure, and fostering regulatory oversight. By addressing these issues, healthcare systems can promote safer injection practices and ensure the well-being of both patients and healthcare providers.

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)0.005
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.286
GPT teacher head0.620
Teacher spread0.334 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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