Evaluating the Impact of a Health Equity Continuing Professional Development Initiative: A Program Evaluation of the Critical Dialogues for Action Series
Bibliographic record
Abstract
INTRODUCTION: Systemic inequities in health care continue to negatively affect marginalized communities, highlighting the need for equity-oriented continuing professional development (CPD). The Critical Dialogues for Action (CDFA) Series was developed to foster health equity, accessibility, and social accountability through dialogue-based workshops. This evaluation aimed to assess the CDFA Series' planning and delivery outputs, and its short-term impacts on participants' attitudes, knowledge, and intentions to apply equity-informed practices. METHODS: A mixed-methods design was used, incorporating quantitative surveys and qualitative feedback from attendees, speakers, and planning committee members. Data sources included program evaluation surveys, postsession feedback surveys, and semistructured interviews. Descriptive statistics and thematic analysis were used to analyze the data, supported by visualizations and participant quotes. RESULTS: Survey respondents (n = 25) and postfeedback participants (n = 90) reported high satisfaction with the CDFA Series' content, relevance, and delivery. Quantitative results showed statistically significant increases in perceived knowledge after session participation ( p = .014). Thematic analysis revealed increased awareness of equity principles, intent to apply new strategies in professional roles, and appreciation for inclusive, reflective dialogue. Speakers and committee members highlighted strong coordination and a desire for ongoing enhancements in diversity and engagement. DISCUSSION: Findings demonstrate the CDFA Series' effectiveness in supporting equity-focused learning and fostering a professional community of practice. These results underscore the potential of dialogue-based CPD initiatives to promote critical reflection and real-world application of health equity principles in diverse professional contexts.
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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 | 0.036 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".