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Record W2776730912 · doi:10.36834/cmej.36863

A needs assessment on addressing environmental health issues within reproductive health service provision: Considerations for continuing education and support

2017· article· en· W2776730912 on OpenAlexaffvenueabout
Linzi Williamson, Sarah L. Sangster, Melanie Bayly, Kirstian Gibson, Megan Clark, Karen Lawson

Bibliographic record

VenueCanadian Medical Education Journal · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsService (business)Needs assessmentMedical educationContinuing educationNursingPsychologyMedicineBusinessPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: This needs assessment was initially undertaken to explore the beliefs and knowledge of nurses and physicians about the impact of environmental toxicants on maternal and infant health, as well as to describe current practice and needs related to addressing environmental health issues (EHI). METHODS: One hundred and thirty-five nurses (n = 99) and physicians (n = 36) working in Saskatchewan completed an online survey. Survey questions were designed to determine how physicians and nurses think about and incorporate environmental health issues into their practice and means of increasing their capacity to do so. RESULTS: Although participants considered it important to address EHIs with patients, in actual practice they do so with only moderate frequency. Participants reported low levels of knowledge about EHIs' impact on health, and low levels of confidence discussing them with patients. Participants requested additional information on EHIs, especially in the form of online resources. CONCLUSION: The results suggests that while nurses and physicians consider EHIs important to address with patients, more education, support, and resources would increase their capacity to do so effectively. Based on the findings, considerations and recommendations for continuing education in this area have been provided.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.413
Teacher spread0.390 · 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 designNot applicable
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

Citations4
Published2017
Admission routes3
Has abstractyes

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