Creating, Synthesizing, and Sharing: The Management of Knowledge in Public Health
Bibliographic record
Abstract
Objectives: To better understand the applicability of knowledge management (KM) in public health (PH) as a strategy to improve planning and decision making. Design and Sample: The study was designed as a narrative inquiry; a form of storytelling research. Qualitative data were collected through interviews designed to gain participants stories about planning processes. Twenty-four participants from six PH Units in Ontario, Canada. Measures: We performed a secondary analysis to better understand the use of KM strategies, techniques, and approaches. Findings were compared to a preliminary KM framework supporting knowledge processes within a dynamic, interactive context. Results: Analysis showed that while KM strategies are supported informally, it is most often done in an ad hoc manner. Participants acknowledged a gap in their knowledge sharing practices. Conclusion: PH professionals are ready to apply KM in PH as an approach to facilitate planning and decision making. The proposed KM framework incorporates partnerships to adapt to the realities of PH context. Consideration of KM strategies can improve information organization, partnerships and decision making, as well as contribute to current PH reforms aimed at strengthening the health care system. This presents an opportunity to integrate formalized methods of knowledge use and knowledge sharing among PH employees using a KM approach.
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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 | 0.038 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".