Assessing the publication productivity of clinical psychology professors in Canadian Psychological Association-accredited Canadian psychology departments.
Bibliographic record
Abstract
Canadian clinical psychology professors in programs accredited by the Canadian Psychological Association (CPA) are generally expected to perform in 3 major domains–research, teaching, and service. Measurement of performance in these domains is complicated. Research productivity, as measured by publication and citation counts, are often touted as objective metrics for evaluating professorial research performance; however, such quantifications can be problematic. Despite concerns, evaluators continue to use publication and citation counts for evaluating psychology professors. Use of these metrics without normative data is extremely problematic; moreover, without ceiling reference points or identification of outliers, new professors and those evaluating them have no perspective on reasonable expectations. The current study provides normative data and ceiling reference points using publically available data for the 255 professors currently in CPA-accredited Canadian clinical psychology programs, as well as submissions from an invited subset of those same professors. The data were stratified by professorial rank and sex, with the men and women having the highest publication and citation counts identified to create ceiling references. The results suggest that most CPA-accredited Canadian clinical psychology professors publish between 0 and 4 articles annually. Men publish significantly more than women at the Assistant and Full professorial ranks (p .05), but not at the Associate rank (p .10). Evidence also suggests that professors cannot be appropriately rank-ordered based on any single research index. Comprehensive results, implications, limitations, contextually based caveats, and directions for future research are discussed.
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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 | 0.037 | 0.255 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.039 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".