Impact of no publicly accessible prenatal education programming on patients and their care providers: a descriptive qualitative study in Nova Scotia, Canada
Bibliographic record
Abstract
OBJECTIVE: Patients in Nova Scotia do not have access to public prenatal education programming. This study aimed to explore whether care providers find patients are uninformed or misinformed, and the impact of that on patients and their care providers with a focus on clinical outcomes, time, resources and informed decision-making. METHODS: Semistructured interviews were conducted with 13 care providers around Halifax and Cape Breton. An interview guide (supplemental) of open-ended questions was used for consistency. A descriptive qualitative approach was employed to describe the contents of the interviews. Each interview was audio-taped and transcribed verbatim by an interdependent transcriber. Transcripts were analysed using established techniques in qualitative descriptive research including coding, grouping, detailing and comparing the data using NVivo V.12 software. A co-coder (SS) independently coded two interviews for inter-rater reliability. RESULTS: The study revealed six themes: (1) concern for a significant population of Nova Scotians experiencing pregnancy, birth and postpartum uninformed and misinformed, (2) consequences for patients who are uninformed and misinformed, (3) more time and resources spent on care for patients who are uninformed or misinformed, (4) patients and their care providers need a publicly available education programme, particularly vulnerable populations, (5) emphasis on programme quality and disappointment with the programme previously been in place and (6) recommendations for an effective prenatal education programme for Nova Scotians. CONCLUSIONS: This study shows care providers believe a public prenatal education programme could improve health literacy in Nova Scotia. Patients are seeking health education, but it is not accessible to all and being uninformed or misinformed negatively impacts patients' experiences and outcomes. This study revealed excess time and resources are being spent on individualised prenatal education by care providers with high individual and system-wide cost and explored the complicated process of providing patient-centred care for people who are uninformed or misinformed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".