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Record W2606964907 · doi:10.47339/ephj.2015.128

Knowledge translation and the Public Health Inspector

2015· article· en· W2606964907 on OpenAlexfundvenueaboutno aff
Charlene Tang, Environmental Health BCIT School of Health Sciences, Bobby Sidhu, Dyt Fong

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
FundersBritish Columbia Institute of Technology
KeywordsKnowledge translationPublic relationsGovernment (linguistics)Public healthThe InternetProcess (computing)BusinessInformation DisseminationKnowledge managementInternet privacyMedicinePolitical scienceNursingComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Knowledge translation (KT) is the process of using the best available knowledge to inform decision-making. Public Health Inspectors (PHIs) are tasked with the critical responsibility of protecting public health. However, there is little data available about how effective and consistent current methods of distributing information to professionals across Canada are. The efficacy of KT has implications on the PHI profession and ultimately, public health protection. Objective: The purpose of this research is to identify how PHIs across Canada take evidence and incorporate it into practice. Methods: A survey was created with questions focused on determining what information PHIs use when making public health decisions, how PHIs go about finding the information required, and the level of trust invested into each source of data. Questions were formulated with guidance from the National Collaborating Centre of Environmental Health (NCCEH). It was distributed electronically to PHIs via social media and BCIT. Results: PHIs use evidence-based information to advise their decisions and actions always (43%) or often (46%) in daily practice. Government agencies, professional organizations, peer-reviewed literature, and colleagues are most often used and deemed as reliable resources. Although very frequently used, the internet was seen as neither reliable nor unreliable. 77% of respondents cited that barriers exist that impede their access to evidence-based information. The most common barriers listed were time constraints, costs, and lack of relevant information. Conclusions: The internet is becoming an increasingly popular means by which knowledge is delivered. However, web-based public health resources need to be more concise, easily accessible, PHI-specific and facilitated by reliable entities to effectively address barriers to practice. Increased communication of evidence, practices, and standards are required between health authorities, government agencies, and PHI professionals to ensure consistent and cohesive protection of public health.

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)0.088
metaresearch head score (Gemma)0.187
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.332
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0880.187
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0190.031
Scholarly communication0.0260.012
Open science0.0040.019
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0190.003

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 head0.295
GPT teacher head0.455
Teacher spread0.160 · 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
GenreCommentary

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

Citations1
Published2015
Admission routes3
Has abstractyes

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