Interprofessional impressions among nursing and pharmacy students: a qualitative study to inform interprofessional education initiatives
Bibliographic record
Abstract
BACKGROUND: Medical care is increasingly complex and must draw upon the distinct, yet complementary skills of various health disciplines. Healthcare student integration through interprofessional education (IPE) activity is considered one way to promote early, and subsequently sustain, the principles of teamwork. However, It has been demonstrated that each profession has distinct profession-based subcultures, or common attitudes, beliefs and values, even among undergraduate students before commencing their training. We sought to evaluate if undergraduate pharmacy and nursing student in the Middle East had similarly formed attitudes and perceptions of each others' roles. METHODS: Focus group and semi-structured interviews were conducted with undergraduate pharmacy and nursing students enrolled at Qatar University College of Pharmacy and University of Calgary - Qatar Nursing programs. An eight-question topic guide was developed following comprehensive literature review of reports of other interdisciplinary assessments (either quantitative and qualitative). Working theories were drawn by the two primary investigators based on relevant topic characteristics such as expressed roles and purposes for interacting with one other, patients, and physicians, to develop explanatory constructs for the findings and identify patterns in the data. Qualitative analysis of interviews were supported by NVivo10 (©) (QSR International 2013) software. RESULTS: One shared themes across both health professional groups evolved during data analysis: perceptions of collaborative roles. Discipline specific themes included pharmacist knowledge and visibility (nursing students) and nurses as informants and roles in total patient care (pharmacy students). As expected, students with little or no curricular-based structured experiential training yet largely drew upon personal experiences, whereas senior students, who did have some amount of professional context, often mirrored those that have been found in other studies investigating this interdisciplinary partnership in the clinical setting. Basic understanding of one another's roles were exhibited, but tended to closely follow traditional scripts that are particularly pervasive in the Middle East. CONCLUSION: Concepts arising from our work reinforces the importance of reaching interdisciplinary understanding through assorted formal and informal exposures and can inform ways in which future IPE initiatives can be developed among the various health professional training programs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".