Health care providers’ perceptions of barriers, facilitators, and acceptability of an eHealth resource: Descriptive study
Bibliographic record
Abstract
Introduction: Women often experience breastfeeding difficulties leading to premature supplementation and cessation. Implementing evidence-informed eHealth resources in clinical interactions with health care providers (HCPs), both in the health department and hospitals throughout a health region may help address this longstanding clinical issue. However, the provision of an eHealth resource alone is not sufficient to increase breastfeeding rates and health literacy, as the effective implementation of the eHealth resource in clinical practice is required. As such, our study objective was to conduct a needs assessment with HCPs to determine the barriers, facilitators, and perceived acceptability of the eHealth resource. Methods: A non-experimental descriptive study was used to examine HCPs’ perceptions of barriers and facilitators to the implementation of an eHealth resource in clinical settings. HCPs completed an online questionnaire to determine barriers and facilitators informed by the Consolidated Framework for Implementation Research (CFIR). Results: HCPs (n=44) completed the survey and agreed the resource was credible (86%), up to date (84%), covered relevant topics (91%), would ease their ability to provide breastfeeding education (82%), and would increase consistent messaging (86%). They agreed it would increase parents’ breastfeeding health literacy (91%) and help parents meet their breastfeeding goals (86%). Concerns were expressed regarding how this would be used in clinical interactions due to challenges with navigation, searchability, and the large amount of content. Implications and Conclusion: HCPs rated the eHealth resource highly; however, adaptations to the local context are required. For effective implementation of interventions in clinical settings, HCPs’ perceptions should be explored to determine their specific needs and determine how to best adapt the intervention to their setting to increase acceptability and facilitate use in clinical interactions.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".