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The State of the Art in Evaluating the Performance of Assistant and Associate Deans as Seen by Deans and Assistant and Associate Deans

2008· article· en· W2229805373 on OpenAlexaboutno aff
David G. Dunning, Timothy M. Durham, Mert N. Aksu, Brian Lange.

Bibliographic record

VenueJournal of Dental Education · 2008
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsnot available
FundersAmerican Dental Education Association
KeywordsPsychologyMedical educationEmployee Performance AppraisalJob satisfactionNursingMedicineSocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.099
metaresearch head score (Gemma)0.193
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.526

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0990.193
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.010
Science and technology studies0.0030.014
Scholarly communication0.0170.010
Open science0.0040.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.043
GPT teacher head0.463
Teacher spread0.420 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations5
Published2008
Admission routes1
Has abstractyes

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