Managing Emergent Knowledge: Addressing the Competency Expectations of Biomedical Employers
Bibliographic record
Abstract
Biomedical graduate students face an uncertain job market. A significant number of these graduates are sub or un-employed and work in areas not requiring a university degree. For those graduates experiencing this, feeling they have no control over their careers, future sub-employment has become a significant contributor to the rise in mental illness among this cohort (Frank & Hou, 2018). The government of Alberta has begun to communicate expectations that university education and training should be tied to labor market expectations, so this study surveyed and interviewed 92 biomedical hiring managers in western Canada. When asked which non-technical skills they felt graduate degree holders typically are missing, 85 percent of respondents indicated that project management and/or customer engagement were the skills that were lacking in recent graduate students in this field of study. The responses received from these leaders in the Biomedical field who were surveyed suggest that a skills awareness gap is preventing employers from understanding the full value of graduates because these graduates do not articulate the professional skills that they gain in graduate school throughout the hiring process or demonstrate their competencies in the workplace. Accordingly, these shortfalls can be addressed by introducing project management and knowledge translation awareness into curricula. Demand for project management expertise is rising in the biomedical field. Greater awareness and exposure to project management and customer engagement through knowledge translation will help prepare students for the transition into their professional field of work, while also making them more productive in their educational program. Likewise, stakeholder (i.e., customers) interaction such as students presenting their research to stakeholders can promote knowledge translation while introducing students to potential employers earlier in their training.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".