Uncovering Tacit Knowledge: A Pilot Study to Broaden the Concept of Knowledge in Knowledge Translation
Bibliographic record
Abstract
BACKGROUND: All sectors in health care are being asked to focus on the knowledge-to-practice gap, or knowledge translation, to increase service effectiveness. A social interaction approach to knowledge translation assumes that research evidence becomes integrated with previously held knowledge, and practitioners build on and co-create knowledge through mutual interactions. Knowledge translation strategies for public health have not provided anticipated positive changes in evidence-based practice, possibly due in part to a narrow conceptualization of knowledge. More work is needed to understand the role of tacit knowledge in decision-making and practice. This pilot study examined how health practitioners applied tacit knowledge in public health program planning and implementation. METHODS: This study used a narrative approach, where teams from two public health units in Ontario, Canada were conveniently selected. Respondents participated in individual interviews and focus groups at each site. Questions were designed to understand the role of tacit knowledge as it related to the program planning process. Data were analyzed through a combination of content analysis and thematic comparison. RESULTS: The findings highlighted two major aspects of knowledge that arose: the use of tacit knowledge and the integration of tacit and explicit knowledge. Tacit knowledge included: past experiences, organization-specific knowledge, community contextual knowledge, and the recognition of the tacit knowledge of others. Explicit knowledge included: research literature, the Internet, popular magazines, formal assessments (surveys and interviews), legislation and regulations. Participants sometimes deliberately combined tacit and explicit knowledge sources in planning. CONCLUSIONS: This pilot demonstrated that front-line public health workers draw upon both tacit knowledge and explicit knowledge in their everyday lived reality. Further, tacit knowledge plays an important role in practitioners' interpretation and implementation of explicit research findings. This indicates a need to broaden the scope of knowledge translation to include other forms of knowledge beyond explicit knowledge acquired through research. Strategies that recognize and support the use of tacit knowledge, such as communities of practice or networks, may be important components of a comprehensive approach to knowledge translation. This study provides support for further investigation of the role of tacit knowledge in the planning and delivery of effective public health services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".