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Record W2990845096 · doi:10.1177/1744987119883404

Methodological reporting in feasibility studies: a descriptive review of the nursing intervention research literature

2019· review· en· W2990845096 on OpenAlexaff
Tanya Mailhot, Marie‐Hélène Goulet, Marc‐André Maheu‐Cadotte, Guillaume Fontaine, Pierre Lequin, Patrick Lavoie

Bibliographic record

VenueJournal of research in nursing · 2019
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsQuebec Network for Research on AgingUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsConsolidated Standards of Reporting TrialsSample size determinationPsychological interventionResearch designMedicineIntervention (counseling)Systematic reviewNursing researchMEDLINENursingPsychologyStatistics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.385
metaresearch head score (Gemma)0.312
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.632
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3850.312
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.010
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.014
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.992
GPT teacher head0.889
Teacher spread0.103 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations26
Published2019
Admission routes1
Has abstractyes

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