Methodological reporting in feasibility studies: a descriptive review of the nursing intervention research literature
Bibliographic record
Abstract
Background In reaction to weaknesses in feasibility studies reporting, the Consolidated Standards of Reporting Trials (CONSORT) statement published an extension for feasibility studies in 2016. Aim The aim of this study was to systematically review and appraise the reporting of feasibility studies in the nursing intervention research literature based on the CONSORT statement extension for feasibility studies. Method Papers published prior to January 2018 that described feasibility studies of nursing interventions were retrieved. Components of feasibility studies were coded, and code frequencies were analysed. Results The review included 186 papers. Although most papers ( n = 142, 76.3%) included the label ‘pilot’ or ‘feasibility’ in their title, reporting for other components generally did not adhere to one or several CONSORT recommendations. Most papers reported objectives ( n = 116, 62.4%), designs ( n = 95, 51%), or rationales for sample size ( n = 165, 88.7%) that were incongruent with the purpose of feasibility studies. Discussion This review results in two main implications for nursing research. First, we noted that the reporting of feasibility studies is weak. While all papers described feasibility studies, almost half focused exclusively on testing the effectiveness of an intervention. Second, we identified rationales for sample size along with key references that could offer guidance in reporting feasibility studies while being coherent with the CONSORT recommendations.
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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 | 0.177 | 0.426 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.048 | 0.039 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".