A Scoping Review of Maternal and Child Health Clinicians Attitudes, Beliefs, Practice, Training and Perceived Self-Competence in Environmental Health
Bibliographic record
Abstract
Clinicians regularly assess, diagnose and manage illnesses which are directly or indirectly linked to environmental exposures. Yet, various studies have identified gaps in environmental assessment in routine clinical practice. This review assessed clinicians' environmental health practices, attitudes and beliefs, and competencies and training. Relevant articles were sought using a systematic search strategy using five databases, grey literature and a hand search. Search strategies and protocols were developed using tailored mesh terms and keywords. 43 out of 11,291 articles were eligible for inclusion. Clinicians' attitudes and beliefs towards environmental health and routine clinical practice were generally positive, with most clinicians believing that environmental hazards affect human health. However, with the exception of tobacco smoke exposure, environmental health assessment was infrequently part of routine clinical practice. Clinicians' self-competence in environmental assessment was reported to be inadequate. Major challenges were the time required to complete an assessment, inadequate training and concerns about negative patients' responses. Clinicians have strong positive attitudes and beliefs about the importance of environmental health assessments. However, more concerted and robust strategies will be needed to support clinicians in assuming their assessment and counselling roles related to a wider range of environmental hazards.
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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".