Questions and answers about conducting systematic reviews in sport and exercise psychology
Bibliographic record
Abstract
Systematic reviews are used to gain insight into the state of research on a given topic, theory, or process; or to inform the development of guidelines, interventions, and policy or public health strategies. Challenges associated with conducting a systematic review include the rapid increase in the variety of systematic review methods and the number of decisions that researchers must make during the process. The purpose of this paper is to provide succinct responses to common questions researchers face when conducting a systematic review. The manuscript is structured around 13 questions that arise during the systematic review process. The questions span the development stage (e.g. why and where should systematic reviews be preregistered; how to decide on inclusion and exclusion criteria), methodological stage (e.g. how to develop and execute a search strategy), and publication stage (e.g. what should be placed in online supplements). Each question was answered with a concise response with recommendations based on the scientific literature and current advances in systematic review techniques. Researchers who have never conducted a systematic review or who are wishing to reflect on their knowledge and practice in conducting a systematic review will benefit from the up-to-date procedures outlined herein.
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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 | 0.569 | 0.857 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.044 | 0.020 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".