Health needs and public health functions addressed in French public health journals
Bibliographic record
Abstract
BACKGROUND: Our aim, as part of the collaborative study SPHERE (Strengthening Public Health Research in Europe), was to describe public health publications in the French language according to health needs and public health functions. METHOD: All articles published in six French public health journals, and one French/English language Canadian journal, over the period 1995-2004, were retrieved from three electronic databases. Original research articles were indexed by hand according to one main domain of health need, based on Global Burden of Disease categories, and into one of four public health functions. RESULTS: After removing duplicates, 3984 original research articles were identified. Only 51% could be allocated to a health needs code. Of these, 71% were about non-communicable diseases, 25% communicable, maternal and perinatal conditions and 5% injuries. This compared only moderately with the global burden of disease for France (84, 5 and 11%, respectively). The other articles addressed health determinants, such as behavioural or environmental exposures, or a methodological issue. Ninety-two percent of the articles could be assigned a public health function code. Health monitoring and health services research accounted for 80% of references from French journals. Only 9% of articles from French journals were related to prevention, which was lower than that in the Canadian journal (17%). Only 1% of articles dealt with legislation. CONCLUSION: The distribution of articles in French public health journals broadly follows the distribution of health needs. History and data availability may explain the extra research focus on communicable diseases and maternal and child health research. Injuries, and prevention, are topics which appear to be under-represented in French language journals.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.107 | 0.090 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".