Implementation of an Online Learning Module for Health Care Providers to Improve Discussions About Weight Gain with Pregnant Women (FS16-01-19)
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the uptake, satisfaction and intentions of health care providers (HCP) after completing an accredited online learning module focused on how they can support women in achieving a healthy diet, physical activity and appropriate gestational weight gain (GWG). METHODS: Alberta Health Services, University of Calgary, and the ENRICH Research team partnered to launch an accredited e-learning module in June 2018, aimed at HCP (MDs, nurses, RDs, others) involved in perinatal care in Alberta. The aim of the module is to improve HCP knowledge and strategies for discussing healthy GWG and related behaviours with pregnant women. Results were obtained in Dec 2018 from an evaluation survey assessing participants’ perceived changes in knowledge, skills (5-point Likert scale) and intentions (open-ended questions) to incorporate new strategies into their practice in the next 3 months as a result of completing the module. RESULTS: By Dec 2018, 216 people had registered for the module (70 MDs (40%), 61 RNs (28%), 31 RDs (14%) and 54 others); 80 (38%) had completed the module and 77 (96%) of them submitted the evaluation survey Mean satisfaction rating was 4.42/5. Self-assessment of knowledge and skills also increased after completing the module. Registrants reported that the case scenarios and inclusion of tools and resources were important (mean rating 4.2 and 4.1 respectively) components of the module. The majority of registrants found the module easy to navigate and information was appropriate to their learning needs. Most completers agreed (31/77) or strongly agreed (44/77) that they learned something in the module that they will incorporate into practice. Registrants reported that they intended to: improve their counselling strategies (21; by using empowering, client-centred language), use a tool mentioned in the module (11), improve patient education opportunities (8), and find ways to support a collaborative approach to prenatal care (7). CONCLUSIONS: The module appears to positively impact self-reported knowledge of healthy pregnancy weight gain concepts and counselling skills. Supporting health care providers to have effective discussions with pregnant women about lifestyle and GWG is an important step towards better compliance with recommendations and improving perinatal outcomes. FUNDING SOURCES: Funding was provided through a Collaborative Research and Innovation Opportunity (CRIO) Program grant from Alberta Innovates as part of the ENRICH research program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".