The State of the Art in Evaluating the Performance of Assistant and Associate Deans as Seen by Deans and Assistant and Associate Deans
Bibliographic record
Abstract
This study explores the little-understood process of evaluating the performance of assistant and associate deans at dental colleges in the United States and Canada. Specifically, this research aimed to identify the methods, processes, and outcomes related to the performance appraisals of assistant/associate deans. Both deans and assistant/associate deans were surveyed. Forty-four of sixty-six deans (66.7 percent) and 227 of 315 assistant/associate deans (72.1 percent) completed surveys with both close-ended and open-ended questions. In addition, ten individuals from each group were interviewed. Results indicate that 75-89 percent of assistant/associate deans are formally evaluated, although as many as 27 percent may lack formal job descriptions. Some recommended best practices for performance appraisal are being used in a majority of colleges. Examples of these best practices are having at least yearly appraisals, holding face-to-face meetings, and setting specific, personal performance objectives/benchmarks for assistant/associate deans. Still, there is much room to improve appraisals by incorporating other recommended practices. Relatively high levels of overall satisfaction were reported by both assistant/associate deans and deans for the process and outcomes of appraisals. Assistant/associate deans rated the value of appraisals to overall development lower than did deans. Qualitative data revealed definite opinions about what constitutes effective and ineffective appraisals, including the use of goal-setting, timeliness, and necessary commitment. Several critical issues related to the results are discussed: differences in perspectives on performance reviews, the importance of informal feedback and job descriptions, the influence of an assistant/associate deans' lack of tenure, and the length of service of deans. Lastly, recommendations for enhancing performance evaluations are offered.
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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 | 0.099 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".