Research activity among diagnostic and therapeutic radiographers: An international survey
Bibliographic record
Abstract
INTRODUCTION: Various national solutions have been considered and implemented to address the factors which limit radiographer engagement in conducting research. Nevertheless, national literature continues to suggest that radiographer engagement in research remains low. The aim of this study was to extend the existing evidence base by conducting an international survey to assess diagnostic radiographers and therapeutic radiographers involvement with, barriers to and support mechanisms for research. METHODS: Data collection was obtained via an online questionnaire which was distributed by the International Society of Radiographers and Radiologic Technologists (ISRRT). The study population included an international sample of qualified diagnostic radiographers and therapeutic radiographers across clinical and academic contexts in a variety of different roles such as clinical practice, management, education and research. RESULTS: In total, 420 diagnostic radiographers and therapeutic radiographers completed the survey. Multiple reasons were identified that were considered to inhibit respondents from conducting research. 69.3% indicated a combination of reasons for lack of engagement with research, rather than one single issue. Examples of reasons include: lack of time, insufficient research funding, limited research expertise, and lack of a suitable mentorship scheme. CONCLUSION: A minor segment of survey respondents indicated involvement in research activity. Lack of dedicated time to research, mentors, and funding were among the main barriers to conduct research. Further research is required to explore what solutions are available to overcoming the barriers.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".