Bibliographic record
Abstract
Background and Objectives Bias in research and the methods used for developing public health guidelines may put the public’s health at risk. This dissertation explores three possible sources of influence on the recommendations made in public health guidelines: • Commercial Influences on Nutrition Research: Primary research studies and systematic reviews form the evidence base for dietary guidelines. The association between funding sources and the outcomes of nutrition studies was therefore explored; • Methods Used for Public Health Guideline Development: Heterogenous methodologies used in the development of public health guidelines may lead to conflicting recommendations. I conducted a systematic analysis of the methods used in their development; • Social Influences on Public Health Guideline Development: The interactions within guideline groups may be a significant influence on the final recommendations made. I aimed to understand the experiences of the participants involved in developing public health guidelines. Methods My methods included: 1) Meta-analysis and systematic review to measure bias in primary nutrition research; 2) Content analysis to understand the methods used in synthesising evidence for public health guidance development; and 3) Qualitative analysis of interviews to understand social influences on guideline development. Results My major findings were: I found an association with industry sponsorship with the outcomes of studies, even when controlling for the internal validity between the studies; I established heterogenous methodologies are being used by organisations that conduct hazard identification and risk assessment; and I identified that the public health guideline process in Australia is a divided one. Conclusions Through greater transparency of funding practices, the development of nutrition study registries and improvements in risk of bias tools used to evaluate the evidence, industry influence on the outcomes of nutrition studies relevant to dietary guidelines can be accounted for. Further, the use of standardised, transparent methodological processes and collaboration between systematic review teams and guideline groups will lead to increased comparability and validity of guidelines and ensure that the recommendations made from them will protect the public’s health.
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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 | 0.698 | 0.888 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".