A Comparison of Two Methods of Needs Assessment: Implications for Continuing Professional Education
Bibliographic record
Abstract
Needs identification is an important component of program planning in continuing professional education. Learners, professional associations, and society all have a stake in ensuring that programs are relevant and focused on important educational needs of professionals. This study compared two different methods of identifying learning needs--perceived needs and knowledge-based needs--for a group of practicing pharmacists (N= 113). The Canadian Consensus Asthma Management Guidelines (1996) provided the framework for the needs assessments and the standard against which pharmacists' knowledge of asthma treatment was assessed. Using data collected via a questionnaire, rank correlation tests showed no relationship between perceived needs and knowledge-based needs. While there was correspondence between the two methods on a few items, overall they did not identify the same needs. This confirmed the results of other research that there are some educational needs of which learners are unaware. Even with the limitations of perceived needs, few continuing professional educators would advocate abandoning this method, although most advocate a combination of methods. The following article discusses the implications of these and other research findings, and current literature on needs assessment in continuing professional education. Many questions remain, however, and there is a need for more research on needs assessment in continuing professional education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".