Australian and New Zealand birth cohort studies: Breadth, quality and contributions
Bibliographic record
Abstract
OBJECTIVE: To understand factors associated with successfully conducting longitudinal studies across early childhood by (i) describing the designs of Australian and New Zealand birth cohort studies; (ii) describing the breadth of variables studied; and (iii) identifying factors influencing study quality, productivity and contributions. METHODS: A systematic review was undertaken of 13 birth cohort studies. Data were collected from published papers and questionnaires administered to study investigators. RESULTS: These single cohort studies recruited children prior to their first birthday. Using an ecological model as an organizing framework, the studies were found to have contributed to knowledge about health and development in multiple domains and well beyond early childhood. Areas for increased focus included policy-relevant environmental factors such as child-care and rural/urban differences, and the influence of fathers, family factors, peers, social competence, and genetic factors. Investigators' responses indicated desires for an increased depth of data collection and stronger sampling designs. Data quality was enhanced by maintaining skilled staff and reduced by inadequate data for tracking study participants. Productivity ranged from 1 to 760 publications per study (median = 14.0). The most productive studies were those that had started before 1990, had representative samples, ongoing funding, and larger research teams. Policy impacts have included changed infant sleeping practices, reduced environmental lead emissions and regulated swimming pool safety standards. CONCLUSIONS: A suite of coordinated and nested studies could meet sampling needs and facilitate depth of inquiry. Stable, long-term funding is required to staff and maintain well-designed longitudinal studies that can address priority research areas and contribute to the development of health and social policy.
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 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 | 0.319 | 0.424 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".