Postdoctoral scholars’ perspectives about professional learning and development: a concurrent mixed-methods study
Bibliographic record
Abstract
Abstract Postdoctoral scholars pursue diverse career paths requiring broad skill sets; however, little is known about postdoctoral scholars’ perspectives about their professional learning, and development needs. The objective of this mixed-methods study was to identify current professional learning and development opportunities used by postdoctoral scholars to obtain the required broad skills sets of value for a changing career landscape. A concurrent mixed-methods design was utilized including a cross sectional survey and qualitative interviews. Analysis was conducted using descriptive statistics and thematic analysis; quantitative and qualitative findings were then triangulated for convergent themes. Key findings indicate that although postdoctoral scholars engage in a variety of professional learning, the perceived usefulness of these sessions varies widely, and the types of professional learning and development that they engage in, may not best support the realities of their future careers. Given the significant resources often required to support professional learning and development initiatives, a deeper understanding and alignment of postdoctoral scholars needs with provided opportunities may help to ensure scarce resources are invested in the most useful and effective strategies.
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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 | 0.065 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".