Survey-Based Evaluation of the Impact and Effectiveness of a Nuclear Medicine Training Program: An Insight from Graduate Trainees
Bibliographic record
Abstract
BACKGROUND: Residency and fellowship training in nuclear medicine aims to equip specialists with diagnostic, technical, clinical, and research skills essential for clinical and academic roles. This study evaluates the effectiveness of a nuclear medicine training program as perceived by former trainees, focusing on mentorship, resources, curriculum content, and professional development. OBJECTIVE: The objective of the study was to evaluate the perceived impact of nuclear medicine training programs on clinical competence, professional development, and career readiness, based on feedback from former trainees. METHODS: In 2024, an online survey was administered to former trainees of the nuclear medicine training program of the University of Ottawa, Canada, across multiple countries, including Canada, Saudi Arabia, Iran, and Oman. Responses from the 11 participants assessed satisfaction with mentorship, facilities, off-service rotations, and skill application post-training. Statistical analysis, including analysis of variance (ANOVA), evaluated the program's impact. RESULTS: The program received high ratings for overall quality (45% (n=5) excellent, 35% (n=4) very good) and its contribution to professional success (85% (n=9) significantly or very significantly). The program was rated very effective by 65% (n=7) of respondents. Key strengths included hands-on training, access to technology, and diagnostic skill development. Areas for improvement included mentorship consistency, advanced equipment access, and structured leadership training. CONCLUSIONS: The nuclear medicine residency and fellowship program effectively enhances diagnostic and clinical competencies. Enhancing mentorship, technology access, leadership development, and research support will optimize training outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".