Assessing the Impact of Substance Abuse on Occupational Injuries using Workers Compensation Claims in Ohio
Bibliographic record
Abstract
Higher education institutions face a risk of knowledge loss when they fail to implement strategies for transferring knowledge from experienced to less experienced employees. Canadian higher education managers are particularly concerned about the adverse impact of losing explicit and valuable tacit knowledge. Grounded in the socialization, externalization, combination, and internalization model, this qualitative pragmatic inquiry study identified and explored successful strategies employed by eight Canadian higher education managers to capture and transfer knowledge from experienced employees, thereby sustaining performance. Data were collected through semistructured interviews and a review of public documents, including strategic plans, annual reports, and accountability statements. Through thematic analysis, three themes were identified that could assist higher education institutions with knowledge transfer, including implementation of (a) a knowledge-sharing culture, (b) mentoring and coaching, and (c) technology adoption. A key recommendation is for higher education managers to integrate formal, structured knowledge management processes with people-centric social learning methods, such as mentorship, to effectively capture explicit and tacit knowledge. The implication for positive social change may include the professional development of individuals and the reduction of knowledge gaps while ensuring business continuity within the community.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".