The Use of Patient Engagement to Gather Perceptions on the Cost of Infant Feeding
Bibliographic record
Abstract
Purpose: Patient-oriented research (POR) and patient engagement (PE) has highlighted the value of incorporating patients' ideas and priorities in health research. Using the guiding principles of POR and PE, the current study conducted PE sessions to gain insight on the perceptions of mothers regarding the costs of infant feeding. Methods: Four patient engagement sessions were held with mothers residing in Newfoundland and Labrador between November 2019 and January 2020. Mothers were targeted through the Brighter Futures Coalition of St. John's, a not-for-profit community organization. PE sessions were designed in a two-hour format, allowing the research team to engage mothers and identify costs of infant feeding from a mothers' perspective. Results: Through the guiding principles of patient-oriented research and patient engagement, our research team successful engaged with mothers in discussions surrounding the costs of infant feeding. The sessions allowed for an in-depth discussion surrounding monetary costs (eg, incidentals of breast or formula feeding), the associated costs of infant feeding and the workplace (eg, perceived productivity) and environment impacts (eg, single use plastics). During each session, evaluations were provided to solicit feedback on whether the goals and expectations of mothers had been met, and whether they felt their opinions were heard and understood. Conclusion: By conducting patient engagement sessions, informed by patient-oriented research guiding principles, we were able to successfully recruit and engage mothers in discussions that led to a better understanding of their perspectives on the costs of infant feeding.
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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 | 0.089 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".