A longitudinal, narrative study of professional socialisation among health students
Bibliographic record
Abstract
PURPOSE: Interprofessional collaboration (IPC) among health professionals is well-recognised to enhance care delivery and patient outcomes. Emerging evidence suggests that the early socialisation of students in health professional programmes to teamwork may have a positive impact on their future as collaborative practitioners. With a purpose of contributing to growing evidence on the processes of professional identity construction, and to explore how early expectations and perceptions of IPC develop during professional socialisation and pre-licensure education, our study examined the early professional socialisation experiences among five groups of health professional students. METHOD: A qualitative, narrative approach was used to examine early professional socialisation among five programmes of health professional students (dentistry, medicine, nursing, pharmacy, physiotherapy) at an Atlantic Canadian University. In March and October 2016, students participated in interviews after first term (n = 44) and first year of study (n = 39). Interviews focused on participants' professional identify formation, as well as their perceptions and experiences of IPC. The authors analysed interview transcripts using narrative analysis. RESULTS: Findings identify that despite the espoused importance of IPC within health professional training, students have a limited understanding of their professional roles and are largely focused on developing a uniprofessional, vs. interprofessional identity. Clinical experiences, role models and exposure to teamwork are critical to contextualise collaborative practice and enhance the development of an interprofessional identity. CONCLUSIONS: Findings can be used to guide the development of curricula that promote interprofessional identity development and IPC during early professional socialisation.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".