Barriers and facilitators for early career researchers completing systematic or scoping reviews in health sciences: A scoping review
Bibliographic record
Abstract
BackgroundSystematic and scoping reviews are being published in health sciences and medicine at an increasing rate. At each stage during the systematic or scoping review cycle, different challenges can arise, especially for a novice researcher. Some of these challenges relate to inadequate or limited training in research methods, reporting standards, and the publication cycle, resulting in poorly conducted or reported reviews being published. We aimed to identify the challenges and facilitators experienced by early career researchers when undertaking systematic and scoping reviews. MethodsUsing a scoping review approach, we conducted comprehensive searches in multiple databases. The selection criteria for screening were established a priori and pilot tested. We included studies that focused on scoping or systematic reviews undertaken by early career researchers in the health sciences and medicine. All levels of screening were performed by two independent reviewers, while conflicts were resolved by discussion or a third reviewer. Two reviewers independently extracted relevant data using a pre-tested form, and discrepancies were resolved through discussion. Results were analysed thematically.ResultsThe literature search yielded a total of 14967 citations. Upon completion of title and abstract screening, 148 references were deemed potentially relevant and reviewed. Subsequently, 8 documents fulfilled our eligibility criteria and were included. ConclusionThis scoping review provides an overview of the barriers early career researchers face when conducting systematic and scoping reviews such as time, experience and expertise, training and mentoring, and methods. We also found facilitators that can be harnessed to assist them including training and adhering to reporting guidelines.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Incentives · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.647 | 0.777 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.025 | 0.030 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".