Perceptions of readiness for interprofessional learning among Ethiopian medical residents at Addis Ababa University: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Interprofessional learning is an important approach to preparing residents for collaborative practice. Limited knowledge and readiness of residents for interprofessional learning is considered one of the barriers and challenges for applying Interprofessional learning. We aimed to assess the perceptions of readiness of medical residents for interprofessional learning in Ethiopia. METHODS: We conducted a parallel mixed-methods study design to assess the perceptions of readiness for interprofessional learning among internal medicine and neurology residents of Tikur Anbessa Specialized Teaching Hospital in Addis Ababa, Ethiopia, from May 1 to June 30, 2021. One hundred one residents were included in the quantitative arm of the study, using the Readiness for Interprofessional Learning Scale (RIPLS) tool. All internal medicine and neurology residents who consented and were available during the study period were included. SPSS/PC version 25 software packages for statistical analysis (SPSS) was used for statistical analysis. Descriptive statistics were summarized as mean and standard deviation for continuous data as well as frequencies and percentages to describe categorical variables. Data were presented in tables. In addition, qualitative interviews were undertaken with six residents to further explore residents' knowledge and readiness for IPL. Data were analyzed using a six-step thematic analysis. RESULTS: Of the 101 residents surveyed, the majority of the study participants were male (74.3%). The total mean score of RIPLS was 96.7 ± 8.9. The teamwork and collaboration plus patient-centeredness sub-category of RIPLS got a higher score (total mean score: 59.3 ± 6.6 and 23.5 ± 2.5 respectively), whereas the professional identity sub-category got the lowest score (total mean score: 13.8 ± 4.7). Medical residents' perceptions of readiness for interprofessional learning did not appear to be significantly influenced by their gender, age, year of professional experience before the postgraduate study, and department. Additionally, the qualitative interviews also revealed that interprofessional learning is generally understood as a relevant platform of learning by neurology and internal medicine residents. CONCLUSIONS: We found high scores on RIPLS for internal medicine and neurology postgraduate residents, and interprofessional learning is generally accepted as an appropriate platform for learning by the participants, which both suggest readiness for interprofessional learning. This may facilitate the implementation of interprofessional learning in the postgraduate medical curriculum in our setting. We recommend medical education developers in Ethiopia consider incorporating interprofessional learning models into future curriculum design.
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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 | 0.004 | 0.011 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".