MétaCan
Menu
Back to cohort
Record W3012894966 · doi:10.1186/s12884-020-2791-8

Gestational weight gain counselling practices among different antenatal health care providers: a qualitative grounded theory study

2020· article· en· W3012894966 on OpenAlexafffundabout
Beth Murray‐Davis, Howard Berger, Nir Melamed, Karizma Mawjee, Maisah Syed, Jon Barrett, Joel G. Ray, Michael Geary, Sarah D. McDonald

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2020
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreSt. Michael's HospitalUniversity of TorontoMcMaster UniversityMcMaster University Medical Centre
FundersUniversity of TorontoMcMaster UniversityCanadian Institutes of Health ResearchSunnybrook Research InstituteHamilton Health Sciences
KeywordsMedicineWeight gainFamily medicinePregnancyNursingReproductive medicineGrounded theoryGestational diabetesHealth careQualitative researchObstetricsGestationBody weight

Abstract

fetched live from OpenAlex

BACKGROUND: Inappropriate gestational weight gain in pregnancy may negatively impact health outcomes for mothers and babies. While optimal gestational weight gain is often not acheived, effective counselling by antenatal health care providers is recommended. It is not known if gestational weight gain counselling practices differ by type of antenatal health care provider, namely, family physicians, midwives and obstetricians, and what barriers impede the delivery of such counselling. The objective of this study was to understand the counselling of family physicians, midwives and obstetricians in Ontario and what factors act as barriers and enablers to the provision of counselling about GWG. METHODS: Semi-structured interviews were conducted with seven family physicians, six midwives and five obstetricians in Ontario, Canada, where pregnancy care is universally covered. Convenience and purposive sampling techniques were employed. A grounded theory approach was used for data analysis. Codes, categories and themes were generated using NVIVO software. RESULTS: Providers reported that they offered gestational weight gain counselling to all patients early in pregnancy. Counselling topics included gestational weight gain targets, nutrition & exercise, gestational diabetes prevention, while dispelling misconceptions about gestational weight gain. Most do not routinely address the adverse outcomes linked to gestational weight gain, or daily caloric intake goals for pregnancy. The health care providers all faced similar barriers to counselling including patient attitudes, social and cultural issues, and accessibility of resources. Patient enthusiasm and access to a dietician motivated health care providers to provide more in-depth gestational weight gain counselling. CONCLUSION: Reported gestational weight gain counselling practices were similar between midwives, obstetricians and family physicians. Antenatal knowledge translation tools for patients and health care providers are needed, and would seem to be suitable for use across all three types of health care provider specialties.

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 imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0090.007
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.052
GPT teacher head0.357
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueBMC Pregnancy and ChildbirthSame topicGestational Diabetes Research and ManagementFrench-language works237,207