963 Iwh research alert – staying current with ohs literature
Bibliographic record
Abstract
Introduction Keeping abreast of the current literature is difficult for any researcher but OHS researchers have particular difficulty because the literature cuts across a variety of fields, such as medicine, public health, psychology, and business. To address this issue, the IWH Library provides a current awareness service called Research Alert. This weekly email provides a listing of recent OHS literature. The alert was originally disseminated to internal researchers but due to popularity is now distributed to external researches and is posted on the Institute’s website. The purpose of this poster is to describe our approach to provide OHS researchers with current, relevant OHS literature as well as highlight and disseminate IWH authored literature. The poster will also describe key elements of the literature retrieved for these alerts. Methods We conducted a citation analysis of an internal database containing the references of literature cited in Research Alert from 2011 – 2016. We note sources for identifying this literature, journals that appear most frequently, journal impact factors. Additional analyses will be conducted on the distribution of these alerts. Results 4997 references were analysed over the six-year period. The alerts average 70 articles per month. JOEM, OEM, JOR, SJWEH, and JCE were the top cited journals. The main methods of identifying literature were hand-searching of journals (n=3331), followed by journal alerts of new issues (n=712), saved database searches (n=563), and suggestions by internal scientists (n=287). Conclusion IWH’s Research Alert highlights and disseminates recent OHS literature from various sources for IWH researchers. While the literature may be located through a number of different mechanisms, we found some specific OHS journals are most relevant for this field. The methods we use to locate and disseminate the literature may be used by others in their field.
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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 | 0.017 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.394 | 0.259 |
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".