A Pilot Study Evaluating the Effectiveness of the 5As of Healthy Pregnancy Weight Gain
Bibliographic record
Abstract
INTRODUCTION: Gestational weight gain (GWG) outside of the 2009 Institute of Medicine guidelines may be harmful to women and their fetuses. Prenatal health care providers (HCPs) are important sources of health information, but not all discuss GWG with their patients. The Canadian Obesity Network's 5As (ask, assess, advise, agree, and assist) of Healthy Pregnancy Weight Gain (5As) is a tool developed to help HCPs counsel their patients on GWG. The main objective of this study was to evaluate the impact of the 5As tool on patient perceptions of GWG discussions with their HCP and to identify suggestions to improve the tool. METHODS: A quasiexperimental study design was conducted whereby HCPs were trained in using the 5As tool (intervention). Patients were then queried at baseline and postintervention using an electronic questionnaire measuring patient-perceived 5As counseling. Inclusion criteria for pregnant women were (1) currently attending their first appointment with participating HCPs, (2) English-speaking, and (3) over 18 years of age. RESULTS: One hundred pregnant women (50 baseline, 50 postintervention) and 15 HCPs (11 midwives, 4 obstetricians) participated. Participants receiving care from 5As-trained HCPs reported scores twice as high (P = .047) in being asked about and were approximately 3 times more likely to be advised an exact amount of target weight gain (P = .03). HCPs suggested improving patient handouts and HCP education on GWG guidelines as well as reducing the content presented in the 5As tool. DISCUSSION: The 5As Tool is effective at initiating HCP-mediated GWG counseling; further research is needed to examine the usefulness of the 5As in clinical practice throughout the length of a full pregnancy. Whether the uptake of the 5As tool contributes to prenatal behavior change remains to be established. Future steps include modifying the tool based on HCP feedback, the development of novel knowledge translation tools, and improved HCP and patient education.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".