Measuring educational workload: a pilot study of paper-based and PDA tools
Bibliographic record
Abstract
BACKGROUND: Teaching is an important professional role for most faculty members in academic health sciences centres. Careful delineation of educational workload is needed to foster and reward teaching efforts, and to facilitate equitable allocation of resources. AIMS: To promote recognition in teaching and facilitate equitable resource allocation, we developed, piloted, and qualitatively assessed a tool for delineating the educational workload of pediatric faculty in an academic health sciences centre. METHODS: A prototype educational workload measurement tool was developed. Between 2002 and 2004, three successive phases of pilot implementation were conducted to (1) assess the face validity of the tool, (2) assess its feasibility, and (3) develop and assess the feasibility of a PDA (Personal Digital Assistant) version. Participants were interviewed regarding strengths, weaknesses, and barriers to completion. Data were analyzed for recurrent themes. RESULTS: Faculty found that the tool was usable and represented a broad range of educational activities. The PDA format was easier to use and better received. Technical support would be imperative for long-term implementation. The greatest barriers to implementation were skepticism about the purpose of the tool and concerns that it would promote quantity over quality of teaching. CONCLUSION: We developed a usable tool to capture data on the diverse educational workload of pediatric faculty. PDA technology can be used to facilitate collection of workload data. Faculty skepticism is an important barrier that should be addressed in future work.
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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 | 0.038 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".