Information Needs of Public Health Staff in a Knowledge Translation Setting in Canada
Bibliographic record
Abstract
Introduction: In response to emerging public health crises in the early 2000s, the Government of Canada recognized the need for a more coordinated public health approach and launched the six National Collaborating Centres for Public Health (NCCPH). The information needs and information-seeking behavior of public health professionals is a relatively understudied area. In this paper, the results of a survey of NCCPH staff is provided and discussed as a means to help fill this gap in the literature. Also examined is the use of information specialists to ascertain whether they are being used to their full potential. Methods: A combination of telephone interviews, a literature review, and a questionnaire distributed to relevant staff. Results: The results indicated some similarities with previous studies such as a reliance on journal articles and colleagues as information sources. It was also shown that staff is unaware of many information resources now available. Training was indicated as a potential area of skills-based growth, as most staff have received limited instruction on searching and information retrieval skills, and required competencies can change frequently as new services, tools, and databases are introduced. Discussion: There is a strong inclination from the staff surveyed to seek information on their own, without the use of an information specialist. However, respondents indicated they are challenged most in their information seeking by a lack of time and awareness of what resources are available, two knowledge areas for which an information specialist is uniquely qualified. Awareness must be raised of the specialized skills of information specialists and how they are able to assist in the information-seeking and retrieval process.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".