A Systematic Review of Factorial Structure of the Iowa Infant Feeding Attitude Scale (IIFAS)
Bibliographic record
Abstract
Background: The attitude towards lactation is one of the best predictors of breastfeeding. Iowa Infant Feeding Attitudes Scale (IIFAS) is used to measure the attitude toward lactation. IIFAS is a valid and reliable tool but factorial structure of this tool was reported various in different studies. The aim of this study is to assess factorial structure of IIFAS. Materials and Methods: An extensive search was done in databases of databases of Medline, EMBASE, Web of Science, Scopus, Cochrane Library, and CINAHL until May 2018. Two independent researchers screened articles and in the next step, full texts of probably relevant articles were read and summarized. The quality of studies was performed by COSMIN checklist. The following keywords were used: (Iowa Infant Feeding Attitude Scale OR IIFAS) AND (Factor Analysis OR exploratory factor analysis OR confirmatory factor analysis OR Validity OR psychometric). Results: Six studies were assessed in systematic review. In Spanish version, single- factor solutions with 9 items in sample of 1,294 pregnancy women was tested and showed a satisfactory fit to the data. In Japanese version, authors provided single-factor- model with 16 items. Factors loading were ranged from -0.06 to 0.68. Arabic version, EFA identified 6 factors with eigenvalues more than 1 explained 61% of total variance. However, scree plot suggested unidimensional structure. In Chinese version, EFA extracted four factors and labeled "Favorable to breastfeeding", "Favorable to formula-feeding", "Convenience" and "Sociological influences". In Canadian and Singapore version, the most sense model based on EFA was a three –factors model and labeled "Favorable to breast feeding", "Convenience" and "Favorable to formula feeding". Conclusion: Four-factor model and three- factor model can be used in clinical practices and research. There is a need to further test single-factor model.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".