Clinic Attendances during the First 12 Months of Life for Aboriginal Children in Five Remote Communities of Northern Australia
Bibliographic record
Abstract
BACKGROUND: The vast majority (>75%) of Aboriginal people in the Northern Territory (NT) live in remote or very remote locations. Children in these communities have high attendance rates at local Primary Health Care (PHC) centres but there is a paucity of studies documenting the reason and frequency of attendance. Such data can be used to help guide public health policy and practice. METHODS AND FINDINGS: Clinic presentations during the first year of life were reviewed for 320 children born from 1 January 2001-31 December 2006. Data collected included reason for infectious presentation, antibiotic prescription and referral to hospital. The median number of presentations per child in the first year of life was 21 (IQR 15-29) with multiple reasons for presentation. The most prominent infectious presentations per child during the first year of life were upper respiratory tract infections (median 6, IQR 3-10); diarrhoea (median 3, IQR 1-5); ear disease (median 3, IQR 1-5); lower respiratory tract infection (median 3, IQR 2-5); scabies (median 3, IQR 1-5); and skin sores (median 3, IQR 2-5). CONCLUSIONS: Infectious diseases of childhood are strongly linked with poverty, poor living conditions and overcrowding. The data reported in our study were collected through manual review, however many remote communities now have established electronic health record systems, use the Key Performance Indicator System and are engaged in CQI (continuous quality improvement) processes. Building on these recent initiatives, there is an opportunity to incorporate routine monitoring of a range of infectious conditions (we suggest diarrhoea, LRTI, scabies and skin sores) using both the age at first presentation and the median number of presentations per child during the first year of life as potential indicators of progress in addressing health inequities in remote communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".