The state of doctoral social work education in Canada
Bibliographic record
Abstract
Doctoral education in social work is critical in nurturing the stewards of the discipline. Universities across Canada, and elsewhere, are increasing admissions for bachelor and master of social work programs. Consequently, doctoral social work programs are expanding to educate and train new social work faculty. Extant literature on doctoral social work education is predominantly American. There are fourteen Canadian doctoral social work programs, yet no study has observed the state of these programs. Using two data sources, this article provides a snapshot of PhD social work student experiences in 2019–2020. The analysis of all doctoral social work students (n = 157) from the 2019 Canadian Graduate and Professional Student Survey (CGPSS) found that: a) the overall quality of social work PhD programs in Canada was rated by students as moderate; and b) financial obstacles may be an undue barrier to academic success. Furthermore, the analysis of an online survey of Canadian social work PhD students (n = 69) regarding their experience applying for doctoral fellowships and scholarships found that workshops significantly facilitated scholarship success, and that other institutional preparation activities were identified as valuable. These findings illuminate the current state of doctoral social work education in Canada with implications for research and education.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".