Determining Clinically Relevant Cutoff Scores for the Iowa Infant Feeding Attitude Scales Among Prenatal Women in Canada
Bibliographic record
Abstract
BACKGROUND: The original 17-item Iowa Infant Feeding Attitude Scale (IIFAS) has been validated and widely used to assess attitudes toward breastfeeding. A reduced 13-item version of the IIFAS was recently validated in a Canadian setting. However, cutoff scores for categorization of infant feeding attitudes on both scales have not yet been established. Research Aim: The aim of this study was to determine optimal cut-ff scores predicting infant feeding attitudes and outcomes for the original and reduced IIFASs. METHODS: A population-based prospective cohort study was undertaken in the Canadian province of Newfoundland and Labrador. A sample of 658 pregnant women were followed up to 1 month postpartum. The receiver operating curve and Youden index were assessed to identify the sensitivity and specificity of cutoff scores. The magnitude at which these scores predicted postpartum feeding outcomes was evaluated using linear regression. RESULTS: Scores of ≤60 (sensitivity = 0.81, specificity = 0.87) and ≤45 (sensitivity = 0.84, specificity = 0.83) for the 17-item and 13-item IIFASs, respectively, were found to be optimal cutoff scores for predicting negative breastfeeding attitudes. The cutoff score for the reduced IIFAS version maintained its ability to predict women who formula-fed at 1 month postpartum (adjusted odds ratio = 6.32, 95% confidence interval = 1.84-11.61) compared with the original scale (adjusted odds ratio = 4.62, 95% confidence interval = 2.42-16.52). CONCLUSION: The proposed cutoff scores for the original and reduced IIFASs have excellent predictive ability to determine infant feeding attitudes and outcomes. The classification of scores enhances the use and applicability of the IIFAS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".