Healthcare professionals’ preferences and needs for continuing professional development activities: A Q-methodology study
Bibliographic record
Abstract
Background Continuing professional development (CPD) provides timely clinical information in the current age of rapid knowledge creation. The exigent COVID-19 pandemic created a scenario that required healthcare educators to adopt alternate CPD delivery models to ensure training continuity. These experiences can shape healthcare professionals’ (HCPs’) preferences and needs, impacting their choice of CPD activities. Methods A cross-sectional, Q-methodology study investigating the preferences and needs of 47 individuals from a range of healthcare professions (physicians, nurses, allied health professionals etc.) was conducted. Three phases of Q-methodology were administered: Q-sample generation, Q-sort exercise and by-person factor analysis. Demographic characteristics like age, geographical location, healthcare discipline, and years of practice were also recorded. Results A Q-sample containing 40 statements related to HCPs’ CPD preferences and needs was derived from the comprehensive literature review and analysis of program evaluation data. The study participants’ demographic characteristics were diverse but evenly distributed (age, occupation), with a large majority practising in Ontario, Canada. Following the Q-sort exercise, an analysis of the respective factor loadings, distinguishing statements and available narrative survey data led to the identification of four factors. These factors represent different types of CPD participants and their training needs. Sixteen participants loaded onto Factor 1 (Value and productivity-focused clinicians), ten participants loaded onto Factor 2 (Application and competency-based learners), ten participants loaded onto Factor 3 (Respite seekers), and three participants loaded onto Factor 4 (Growth-oriented professionals). A single consensus statement that highlighted neutral viewpoints towards the need for CPD activities to have “appropriate difficulty and volume of content” was also identified. Conclusion This study uniquely leveraged on Q-methodology’s ability to study subjectivity using a limited sample, applying it to a diverse interprofessional population. Based on this study’s findings about HCPs’ CPD priorities, CPD providers should adapt their current CPD offerings to better meet contemporary needs.
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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 | 0.054 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".