Indices and perception of crowding in Pacific households domicile within Auckland, New Zealand: findings from the Pacific Islands Families Study.
Bibliographic record
Abstract
AIMS: Pacific peoples (mostly of Samoan, Tongan, Niuean, or Cook Islands origin) have a higher proportion of reported household crowding than any other ethnic group in New Zealand. However, there are multiple ways crowding can be measured. This paper reports the prevalence and concordance of Pacific peoples' own perception of household crowding together with three commonly employed indices, the American Crowding Index (ACI), Canadian National Occupancy Standard (CNOS), and Equivalised Crowding Index (ECI). METHODS: A cohort of Pacific infants born during 2000 in Auckland was followed. Maternal home interviews were conducted at 6-weeks, 12-months, and 24-months postpartum. Household membership information was obtained from the 12-month interviews. Agreement was assessed using the kappa statistic. RESULTS: In total, 1224 mothers completed the 12-month interview. Overall, 30% of mothers perceived crowding to be an issue for their households. Crowding was indicated by ACI for 37%, by CNOS for 32%, and by ECI for 59% of households. Agreement between measures ranged from poor (kappa=0.36) to moderate (kappa=0.61). In regression analyses, self-reported perception of crowding had better validity than ACI, CNOS, or ECI indices. CONCLUSION: Estimated household crowding prevalence depends on the index used. Self-reported perception of crowding appears the best measure and ECI the worst. Regardless of the index used, crowding remains an important problem for Pacific people despite recent initiatives within New Zealand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".