Factors Influencing Effective Communication between Stakeholder Groups during DVM Work-Based Learning Program in Bangladesh
Bibliographic record
Abstract
This study aimed to review the existing communication systems between the universities, placement providers, and students during the DVM final year work-based learning (WBL) program in Bangladesh. The intention was to identify what factors impact the effectiveness of the communication system and to explore ways to enhance communication to better support the program. A questionnaire was used to collect details about the WBL program and the communication systems from all universities in Bangladesh. The questionnaire was completed on paper at a meeting of the National Veterinary Dean Council and online with a member of each university's WBL coordination team. A summary of the current WBL programs in Bangladesh was produced. Focus group discussions were used to collect more detailed information about the communication systems and were held via Zoom with recent graduates ( n = 16) and placement providers ( n = 7). Effective means of communication between all stakeholders were identified as an initial letter, phone calls, and spot visits by teachers. However, the frequency of formal communication before and during placements was variable, and the ways of providing feedback on the communication systems were insufficient. These issues sometimes undermined the student learning experience. Suggestions for improvements included increased resourcing, greater use of online communication systems, and a national committee to oversee WBL. Other ways to motivate placement providers included a better honorarium and continuing education courses. The results suggest that existing communication systems for veterinary WBL in Bangladesh are not completely satisfactory. Measures are needed to improve communication to optimize the student learning experience and capitalize on the many benefits of the WBL program for all stakeholders.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".