Letters to the Editor
Bibliographic record
Abstract
18 September 2006 Dear Editor, LOOKING OUT FOR OVERWEIGHT/OBESITY IN AUSTRALIAN PRESCHOOLERS: WHO’S ACTUALLY DOING IT? Prevalence of overweight/obesity has risen dramatically in Australian pre-schoolers, topping 20% by 2004.1 The period before school entry may be an important opportunity for early intervention, before comorbidities emerge. Despite this rising epidemic, it is clear that major child health providers including general practitioners2 and large children’s hospitals3,4 are neither systematically nor effectively addressing the issue. The only other services universally available for pre-schoolers’ health care in several Australian states are the community maternal and child nursing services. We surveyed metropolitan Victorian Maternal and Child Health (M&CH) nurses regarding their routine anthropometric practice for 2- to 5-year-old children and perceived role in managing childhood overweight. In February 2006, surveys were completed by 175 of a convenience sample of just under 200 M&CH nurses attending one of three annual professional development conferences in Melbourne, Victoria, which all M&CH nurses are encouraged to attend. Respondents’ M&CH experience ranged from <5 to >20 years. Most training in basic anthropometry (70%) had occurred during the M&CH training course, often many years earlier, and >20% reported having received no anthropometric training. All routinely weighed and measured attending children. Most (64%) scales had been calibrated within the preceding year, and 62% were over 5 years old. Height measures tended to be old and rarely checked; 23% reported using stand-alone stadiometers, 63% pull-down tapes (which need regular checking), and the remainder were using techniques likely to be inaccurate. Although 95% recorded height and weight measurements and 80% also plotted them on percentile charts, none used body mass index (BMI) charts, only 12% reported calculating child BMI, and only 22% knew how to calculate BMI correctly. There was no consensus on the prevalence of overweight and obesity. Some nurses reported seeing few or no overweight/obese children while others thought half of all their clients were affected. Although more than 80% felt very/quite competent broaching and discussing a child’s overweight, only half felt very/quite competent regarding making a difference to a child’s weight. These findings closely resemble our previous findings for general practitioners and major children’s hospitals. Clearly, the 2003 National Health and Medical Research Council guidelines5, recommending routine use of BMI as the measure for overweight surveillance, in Australian children are a long way from reality in all major primary care sectors. Substantial, sustained and systematic investments are required for primary care to become an effective player in state or national initiatives to address established overweight/obesity in pre-schoolers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".