The changing postdoc and key predictors of satisfaction with professional training
Bibliographic record
Abstract
Purpose The postdoctoral position was originally created as a short training period for PhD holders on the path to becoming university professors; however, the single-purpose paradigm of training has evolved considerably over time. The purpose of this paper is to report on the opportunities and challenges faced by postdocs as they navigate this complex training period. Design/methodology/approach To better understand the changes in postdoctoral training the Canadian Association of Postdoctoral Scholars – l’Association Canadienne des Stagiaires Postdoctoraux (CAPS-ACSP) conducted three professional national surveys of postdocs working in Canada and Canadian postdocs working internationally. Using the data from each survey, the authors investigated demographics, career goals and mental health and developed a theory-based path model for predicting postdoctoral training satisfaction, using structural equation modeling. Findings The analysis revealed that during their training postdocs face mental health symptoms, which play a role in job satisfaction. Additionally, predictors of satisfaction with career training were opportunities for skills development and encouragement from supervisors. Predictors of satisfaction with compensation were salary, skills training, mental health and encouragement from supervisors. Originality/value This first in-depth analysis of mental health symptoms illuminates the postdoc experience in academia. The study highlights the need for substantive changes to address the challenges facing postdoctoral training in the current research model in North America.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".