Empathy for the health professional in online asynchronous graduate education: an initial design thinking approach to program improvement
Bibliographic record
Abstract
Abstract Introduction Health professionals engage in continuous professional development through higher education. As traditional university learning environments pose a challenge to working health professionals, distance education allows these learners to engage in higher education in alignment with their learning preferences and needs. Literature on health professional learners’ experiences in online learning environments report findings at the course level and/or focus on a singular aspect of the online learning experience. Objectives In this initial study, we aim to understand the health professional learner’s perspective in a distance graduate education program and make normative recommendations to improve the health professional learner experience in an online environment. Methods Within the context of theEmpathyarm of Deitte and Omary’s (Deitte and Omary in Acad Radiol 26:1417–1420, 2019) Design Thinking methodology, we conducted a two-part sequential explanatory qualitative study. Part 1 involved focus groups with stakeholders (n = 14) of the Health Science Education (HSED) Graduate Program at McMaster University (Hamilton, Canada). Part 2 entailed semi-structured interviews with learners (n = 11) in the HSED Program. An unconstrained approach to directed content analysis was used to analyze the data and construct themes. Results An overarching theme of ‘the learner experience is an autonomous-supportive online learning environment’ was constructed, with two sub-themes: learner autonomy and building community. Conclusion Learners valued an autonomous-supportive online learning environment wherein they could tailor their educational experiences while also fostering a sense of community in the online environment. Future research should consider how a balance between maintaining autonomy in pacing learning and building community can be achieved to enhance the health professional learner experience in an online environment.
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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 | 0.042 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".