Non-academic employability of life science PhDs: the importance of training beyond the bench
Bibliographic record
Abstract
ABSTRACT To better understand how PhD graduates have prepared for the non-academic job market, we surveyed life science PhD and postdoctoral graduates from the University of Toronto who were employed in non-academic sectors. We also surveyed life science PhD and postdoctoral trainees to assess their engagement in career preparation activities. PhD professionals employed in non-academic sectors had engaged in various career preparation activities during their training. Some activities had a higher perceived impact on the path to employment than others. Trainees had also engaged in such activities, but those rated by professionals as having a highly positive impact on their path to employment were engaged in by only a minority of trainees. The proportion of trainees who wished to work in a non-academic sector was higher among those who were closer to program completion. Like professionals, many trainees reported facing barriers to pursuing career development activities. Our findings suggest that PhD trainees seeking to work in non-academic sectors should engage in career preparation activities, particularly those that involve experiential learning. By supporting co-curricular programming and reducing barriers to participation in career development activities, academic administrators and faculty have the opportunity to support trainees’ professional development beyond the laboratory.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".