A Bibliometrics-Enhanced, PAGER-Compliant Scoping Review of the Literature on Paralympic Powerlifting: Insights for Practices and Future Research
Bibliographic record
Abstract
Paralympic powerlifting (PP), formerly known as “International Paralympic Committee” (IPC) powerlifting, is the format of powerlifting adapted for athletes with disabilities, and it differs from the version for able-bodied athletes in that it consists of bench press only. According to the mandate of the IPC, PP athletes should be enabled to achieve sporting excellence. As such, rigorous evidence is needed. However, to the best of our knowledge, there exists no systematic assessment of the body of scholarly evidence in the field of PP. Therefore, the present study was conducted to fill in this gap of knowledge, by conducting a scoping review of the literature enhanced by a bibliometrics analysis and by mining two major scholarly databases (MEDLINE via PubMed and Scopus). The aim was to provide a review/summary of the findings to date to help practitioners and athletes. Thirty-seven studies were retained in the present study. These covered the following thematic areas: (i) warm-up strategies (n = 2); (ii) aspects of training (n = 2); (iii) physiological aspects and responses (n = 2); (iv) psychological aspects and responses (n = 2); (v) biomechanics of bench press (n = 8); (vi) recovery strategy (n = 5); (vii) impact of the disability and type of disability (n = 4); (viii) epidemiology of PP (n = 6); and (ix) new analytical/statistical approaches for kinematics assessments, internal load monitoring, and predictions of mechanical outputs in strength exercises and in PP (n = 6). Bibliometrics analysis of the PP-related scientific output revealed that, despite having already become a paralympic sports discipline in 1984, only in the last few years, PP has been attracting a lot of interest from the community of researchers, with the first scholarly contribution dating back to 2012, and with more than one-third of the scientific output being published this year (2022). As such, this scholarly discipline is quite recent and young. Moreover, the community dealing with this topic is poorly interconnected, with most authors contributing to just one article, and with one single author being a hub node of the author network. Distributions of the number of articles and the authors/co-authors were found to be highly asymmetrical, indicating that this research is still in its infancy and has great room as well as great potential to grow. Reflecting this, many research topics are also overlooked and underdeveloped, with the currently available evidence being based on a few studies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".