A needs assessment on addressing environmental health issues within reproductive health service provision: Considerations for continuing education and support
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".