Deciding what kind of course to take: Factors that influence modality selection in accounting continuing professional development
Bibliographic record
Abstract
This study used a cross sectional sample created by self-selection from a researchers' email invitation to accountants in Canada to determine which modalities accountants preferred when selecting Continuing Professional Development (CPD), and the selection factors they used in making those choices. The survey contacted 32,000 accountants in Canada and a total of 428 accountants from across Canada completed the online survey. Live seminars and live webinars were the highest ranked and accountants preferred synchronous over asynchronous courses. The factors most important to accountants are content, cost and CPD hour requirements. The ranking of selection factors for synchronous courses in general did not differ from those for asynchronous courses with the exception of self-paced courses where the selection factors of “pace” and “time away from work” were ranked higher than for other courses. The results of the study indicate a continuing need for providers to ensure that courses are relevant and accessible to accountants. Further research is suggested into the differences noted between genders as well as other categorical differences. Work-life balance was a recurring theme that should also be explored further. Pedagogical use in the design of modalities is a further avenue for future research.
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 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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".