Effects of assessing the productivity of faculty in academic medical centres: a systematic review
Bibliographic record
Abstract
BACKGROUND: Many academic medical centres have introduced strategies to assess the productivity of faculty as part of compensation schemes. We conducted a systematic review of the effects of such strategies on faculty productivity. METHODS: We searched the MEDLINE, Healthstar, Embase and PsycInfo databases from their date of inception up to October 2011. We included studies that assessed academic productivity in clinical, research, teaching and administrative activities, as well as compensation, promotion processes and satisfaction. RESULTS: Of 531 full-text articles assessed for eligibility, we included 9 articles reporting on eight studies. The introduction of strategies for assessing academic productivity as part of compensation schemes resulted in increases in clinical productivity (in six of six studies) in terms of clinical revenue, the work component of relative-value units (these units are nonmonetary standard units of measure used to indicate the value of services provided), patient satisfaction and other departmentally used standards. Increases in research productivity were noted (in five of six studies) in terms of funding and publications. There was no change in teaching productivity (in two of five studies) in terms of educational output. Such strategies also resulted in increases in compensation at both individual and group levels (in three studies), with two studies reporting a change in distribution of compensation in favour of junior faculty. None of the studies assessed effects on administrative productivity or promotion processes. The overall quality of evidence was low. INTERPRETATION: Strategies introduced to assess productivity as part of a compensation scheme appeared to improve productivity in research activities and possibly improved clinical productivity, but they had no effect in the area of teaching. Compensation increased at both group and individual levels, particularly among junior faculty. Higher quality evidence about the benefits and harms of such assessment strategies is needed.
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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 | 0.041 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".