Experiences, Opinions, and Use of Complementary and Alternative Medicine Among Alberta Midwives
Bibliographic record
Abstract
Background: Complementary and alternative medicines (CAMs) are widely used by individuals in many parts of the world to treat different ailments and maintain good health. Midwives are maternity care providers who may recommend or provide CAMs to assist clients with their pregnancies and childbirth and the early neonatal health of infants. There are currently no provincial data on the recommendation and use of CAMs by Alberta midwives. Objectives: To describe the use, experiences, and opinions of Alberta midwives about CAMs, as well as their self-reported educational needs relating to CAM. Method: A descriptive cross-sectional survey was distributed to all midwives registered with the Alberta Association of Midwives. Result: The response rate to the survey was 23.7% and the completion rate was 82.7%. About 90% of the participating midwives recommended CAM, and 45.8% provided CAM very often to their clients. Client preferences and scientific evidence of efficacy were the most commonly stated reasons for recommending CAM. More than two-thirds (70.8%) of respondents believed that they lacked adequate CAM education. Conclusion: CAM was frequently recommended by the midwives who participated in this study. However, the majority of the participants indicated that they lack adequate knowledge and education in regard to CAM. Consequently, providing more CAM education opportunities for midwives may be justified. This article has been peer reviewed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".