Assessing Infant Feeding Attitudes of Expectant Women in a Provincial Population in Canada
Bibliographic record
Abstract
BACKGROUND: Maternal attitudes to infant feeding are predictive of intent and initiation of breastfeeding. OBJECTIVES: The Iowa Infant Feeding Attitude Scale (IIFAS) has not been validated in the Canadian population. This study was conducted in Newfoundland and Labrador, a Canadian province with low breastfeeding rates. Objectives were to assess the reliability and validity of the IIFAS in expectant mothers; to compare attitudes to infant feeding in urban and rural areas; and to examine whether attitudes are associated with intent to breastfeed. METHODS: The IIFAS assessment tool was administered to 793 pregnant women. Differences in the total IIFAS scores were compared between urban and rural areas. Reliability and validity analysis was conducted on the IIFAS. The receiver operating characteristic (ROC) of the IIFAS was assessed against mother's intent to breastfeed. RESULTS: The mean ± SD of the total IIFAS score of the overall sample was 64.0 ± 10.4. There were no significant differences in attitudes between urban (63.9 ± 10.5) and rural (64.4 ± 9.9) populations. There were significant differences in total IIFAS scores between women who intend to breastfeed (67.3 ± 8.3) and those who do not (51.6 ± 7.7), regardless of population region. The high value of the area under the curve (AUC) of the ROC (AUC = 0.92) demonstrates excellent ability of the IIFAS to predict intent to breastfeed. The internal consistency of the IIFAS was strong, with a Cronbach's alpha greater than .80 in the overall sample. CONCLUSION: The IIFAS examined in this provincial population provides a valid and reliable assessment of maternal attitudes toward infant feeding. This tool could be used to identify mothers less likely to breastfeed and to inform health promotion programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".