The use of tacit and explicit knowledge in public health: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Planning a public health initiative is both a science and an art. Public health practitioners work in a complex, often time-constrained environment, where formal research literature can be unavailable or uncertain. Consequently, public health practitioners often draw upon other forms of knowledge. METHODS: Through use of one-on-one interviews and focus groups, we aimed to gain a better understanding of how tacit knowledge is used to inform program initiatives in public health. This study was designed as a narrative inquiry, which is based on the assumption that we make sense of the world by telling stories. Four public health units were purposively selected for maximum variation, based on geography and academic affiliation. RESULTS: Analysis revealed different ways in which tacit knowledge was used to plan the public health program or initiative, including discovering the opportunity, bringing a team together, and working out program details (such as partnering, funding). CONCLUSIONS: The findings of this study demonstrate that tacit knowledge is drawn upon, and embedded within, various stages of the process of program planning in public health. The results will be useful in guiding the development of future knowledge translation strategies for public health organizations and decision makers.
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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 | 0.039 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".