Examining the perception of undergraduate health professional students of their learning environment, learning experience and professional identity development: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: The quality of the learning environment significantly impacts student engagement and professional identity formation in health professions education. Despite global recognition of its importance, research on student perceptions of learning environments across different health education programs is scarce. This study aimed to explore how health professional students perceive their learning environment and its influence on their professional identity development. METHODS: An explanatory mixed-methods approach was employed. In the quantitative phase, the Dundee Ready Education Environment Measure [Minimum-Maximum possible scores = 0-200] and Macleod Clark Professional Identity Scale [Minimum-Maximum possible scores = 1-45] were administered to Qatar University-Health students (N = 908), with a minimum required sample size of 271 students. Data were analyzed using SPSS, including descriptive statistics and inferential analysis. In the qualitative phase, seven focus groups (FGs) were conducted online via Microsoft Teams. FGs were guided by a topic guide developed from the quantitative results and the framework proposed by Gruppen et al. (Acad Med 94:969-74, 2019), transcribed verbatim, and thematically analyzed using NVIVO®. RESULTS: The questionnaire response rate was 57.8% (525 responses out of 908), with a usability rate of 74.3% (390 responses out of 525) after excluding students who only completed the demographic section. The study indicated a "more positive than negative" perception of the learning environment (Median [IQR] = 132 [116-174], Minimum-Maximum obtained scores = 43-185), and a "good" perception of their professional identity (Median [IQR] = 24 [22-27], Minimum-Maximum obtained scores = 3-36). Qualitative data confirmed that the learning environment was supportive in developing competence, interpersonal skills, and professional identity, though opinions on emotional support adequacy were mixed. Key attributes of an ideal learning environment included mentorship programs, a reward system, and measures to address fatigue and boredom. CONCLUSIONS: The learning environment at QU-Health was effective in developing competence and interpersonal skills. Students' perceptions of their learning environment positively correlated with their professional identity. Ideal environments should include mentorship programs, a reward system, and strategies to address fatigue and boredom, emphasizing the need for ongoing improvements in learning environments to enhance student satisfaction, professional identity development, and high-quality patient care.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".