Psychometric Assessment and Precision Remodeling of the Iowa Infant Feeding Attitude Scale to Improve Clinical Use and Efficacy Among Prenatal Women in Canada
Bibliographic record
Abstract
BACKGROUND: The 17-item Iowa Infant Feeding Attitude Scale (IIFAS) has been widely used to assess maternal attitudes toward infant feeding and to predict breastfeeding intention. The IIFAS has been validated among prenatal women located in Newfoundland and Labrador in Canada, although its length may prove challenging to complete in a clinical setting. Research aim: The authors aimed to reduce the number of items from the original 17-item IIFAS scale while maintaining reliability and validity. METHODS: A nonexperimental cross-sectional design was used among 1,283 women in their third trimester residing in Newfoundland and Labrador. Data were collected from August 2011 to June 2016. An exploratory factor analysis using principal component analysis was performed to explore the underlying structure of the IIFAS. The internal consistency of both the 17-item and reduced version was assessed using Cronbach's alpha and item-total correlation. The area under the curve and linear regression model were used to assess predictive validity of intention to breastfeed. RESULTS: Our findings revealed that a 13-item IIFAS (Cronbach's α = .870) had relatively similar internal consistency to the original IIFAS (Cronbach's α = .868). Three themes were extracted from the factor analysis, resulting in the removal of four items. The reduced scale demonstrated an excellent ability to predict breastfeeding intention (area under the curve = 0.914). CONCLUSION: The reduced 13-item version of the IIFAS is a psychometrically sound instrument that maintains its accuracy and validity when measuring maternal feeding attitudes during pregnancy and can be more time efficient in clinical settings compared with the 17-item IIFAS.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".