Needs assessment and current practice of alcohol risk assessment of pregnant women and women of childbearing age by primary health care professionals.
Bibliographic record
Abstract
BACKGROUND: Assessing the current practices and learning and resource needs of primary health care professionals in regards to their alcohol risk assessment practices is an important step in providing optimal training and educational methods. Needs and current practices in alcohol risk assessment of pregnant women and women of child bearing years may vary according to practitioner demographics. METHODS: To appraise alcohol risk assessment current practices and learning and resource needs among Saskatchewan primary health care professionals, a mail and online survey was distributed in the spring of 2006 to family physicians/general practitioners and nurse practitioners. RESULTS: In total, 876 surveys were distributed and 386 were returned for an overall response rate of 44.1%. The majority of survey respondents reported either rarely or never using a standardized screening tool in assessing alcohol risk in women or reported using a standardized screening tool that is less sensitive. Current practices varied according to gender, length of time in practice and practice location, while learning and resource needs were more likely to be identified by nurse practitioners, female physicians, and physicians from rural areas. Physicians who had practiced for less than 5 years were more likely to want an online course. DISCUSSION: Knowing the needs and practices of health care professionals may assist learning and resource training and could assist in teaching best practices in alcohol risk assessment. Assessing alcohol risk in pregnant women and women of childbearing age is critical for prevention of FASD.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".