An Exploratory Thematic Analysis of the Breastfeeding Experience of Students at a Canadian University
Bibliographic record
Abstract
BACKGROUND: The demographic of today's university student includes breastfeeding mothers. Few studies have examined the breastfeeding experience that women face upon their return to school. Research aim: The purpose of this research was to explore the breastfeeding experience of students on a university campus. METHODS: This qualitative study used semistructured interviews. Responses were audiotaped, transcribed, and coded according to common themes using MAXQDA software. RESULTS: A total of eight women were interviewed for the study. All women reported "feelings of isolation" and expressed concern over "what will others think." In addition, "nowhere to breastfeed" and "challenges of pumping" emerged as common barriers to breastfeeding. Regrettably, "forced decisions" emerged as a major theme, with four out of eight women reporting having to supplement with formula because they returned to school. CONCLUSION: Student breastfeeding mothers are faced with emotional and physical challenges upon their return to school. Lack of space to breastfeed or pump as well as lack of support on campus are the main reasons that students stop breastfeeding prematurely. A day care facility on campus that accepts young infants, a Baby-Friendly space, and enhanced education are required to support student mothers in their choice to breastfeed.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| 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".