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Record W2997162614 · doi:10.3928/00220124-20191217-06

A National Survey of Educational and Training Preferences and Practices for Public Health Nurses in Canada

2020· article· en· W2997162614 on OpenAlexaboutno aff
Shannon L. Sibbald, Jathuson Jegatheeswaran, Hayley Pocock, Greg Penney

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceContinuing educationPublic healthWorkforce developmentAgency (philosophy)Workforce planningNursingWork (physics)Medical educationNeeds assessmentProfessional developmentMedicinePublic relationsPsychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: The effective mix of public health professionals has been the focus of recent policies and literature. Information is limited on the preferences for training and continuing education of the Canadian Public Health Workforce. This information could assist in surge capacity efforts and help in evaluating the success of workforce development strategies and recruitment/retention efforts. METHOD: The Canadian Public Health Workforce Survey was conducted in 2015 by the Canadian Public Health Association in collaboration with the Public Health Agency of Canada (PHAC). The survey was conducted to inform an ongoing evaluation of the PHAC's workforce development. This article reports on a subset of the survey: public health nurses (PHNs). RESULTS: The response rate to the survey was approximately 40% (2,075 participants); 470 respondents (22.7%) were PHNs. Challenges faced by PHNs to pursuing continuing education include a lack of targeted training, resources, and coordinated training efforts. CONCLUSION: The results provide insight into potential direction to support the work of PHNs in practice including a need for a coordinated approach to continuing education. Future educational strategies should consider tailored education strategies for PHNs. [J Contin Educ Nurs. 2020;51(1):25-31.].

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.219
GPT teacher head0.521
Teacher spread0.302 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2020
Admission routes1
Has abstractyes

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