Innovative online care: A cross-sectional survey study of potential benefits and challenges of online lactation consultation service with paediatrician consultation
Bibliographic record
Abstract
Background: Telelactation, a virtual lactation support platform, is a convenient and effective way of providing virtual breastfeeding support services and communicating proper breastfeeding techniques to new mothers to address any breastfeeding challenges and boost overall maternal breastfeeding confidence all from the comfort of their homes. This Ontario Health Insurance Plan (OHIP) covered service benefits more mothers in the remote areas where in-person LC service is not easily accessible. Objective: The objective of this cross-sectional survey study is to explore the feasibility, potential benefits, and challenges, and level of patient satisfaction in virtual lactation consultation services along with virtual paediatrician consultation available in Ontario, Canada. Method: Patient satisfaction was assessed using a structured online survey as a part of a cross-sectional observational study that asked questions relevant to several independent demographic variables as well as specific Likert type scale questions to gauge patient satisfaction with virtual lactation consultation. Results: Data were analyzed from 177 survey responses (n=177), of which study revealed that 86.44% (153/177) were satisfied with the virtual lactation services they received. Patient satisfaction was found to be higher in the first-time mothers with high school or undergraduate education between the age group of 26 to 35 years living in the Greater Toronto Area (GTA). In terms of connectivity, participants from the GTA had a better experience overall compared those living outside the GTA. Conclusion: OHIP covered telelactation with a paediatrician consultation is an innovative and feasible health care delivery platform for providing remote professional breastfeeding support to mothers of all socio-economic strata with great potential to further improve both patient experience and efficiency in patient care.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".