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Record W3146810909 · doi:10.1093/jbcr/irab032.299

649 The Quality of Survey Research in Burn Care: A Systematic Review

2021· review· en· W3146810909 on OpenAlexaboutno aff
Dana Anderson, Erin Fordyce, Sebastian Q Vrouwe

Bibliographic record

VenueJournal of Burn Care & Research · 2021
Typereview
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBurn centerScopusWorkforceFamily medicineHealth careMEDLINEInclusion (mineral)NursingMedical emergencyPoison controlPsychology

Abstract

fetched live from OpenAlex

Abstract Introduction Burn care is a relatively small field with contributions from many healthcare disciplines. Further, the practice of burn care varies considerably from center to center and between regions. Given these factors, survey studies are frequently used to better understand practice variations, establish guidelines, and direct future research questions. If survey research is poorly designed or reported, it limits the ability to form meaningful conclusions. Given the prevalence of clinician surveys published in burn care, this study aims to evaluate its quality and to determine which approaches lead to a more successful survey. Methods A systematic review was performed by two independent reviewers using PubMed, Scopus, and Web of Science databases for the dates January 1, 2000 to March 19, 2020. Articles were included if they were published in English and surveyed providers on a topic related to burn care. Surveys of patients, those that evaluated an intervention or included other research designs were excluded. Data related to survey content, methodology, and quality was extracted independently by two reviewers. Results The search identified 7351 non-duplicate citations, of which 247 underwent full text review, and 144 met inclusion criteria. The number of published surveys increased by an average of 21% annually over the study period (P< 0.001). The majority of surveys originated in the United States (40%), United Kingdom (19%) and Canada (7%) and were either national (47%) or international (37%) in scope. The most common themes were education/training/workforce (21%), resuscitation/critical care (17%) and wound care (14%). Burn surgeons/physicians (45%) were the most frequently surveyed population, but all disciplines were represented. The majority of surveys were electronic (51%) and sampled all members of a defined group (72%). Few studies reported the use of reminders (29%) or incentives (2%) to improve survey completion. In terms of quality, the majority did not report any survey development steps (71%) or survey validity/reliability (92%), and half did not include the questionnaire in the manuscript or appendix. A response rate was calculated in 82% of studies. The median (IQR) response rate of all studies was 54% (32–83). A subgroup analysis of national and international studies sent electronically to burn surgeons/physicians (N=28) had a response rate of 40% (26–50). Conclusions Survey research is increasingly published in the burn care literature and covers a range of themes and populations. Despite the limited use of reminders and incentives, survey participation is relatively high. The quality of survey reporting is generally poor, limiting the ability to apply this research into practice.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.137
metaresearch head score (Gemma)0.426
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.863
Threshold uncertainty score0.726

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1370.426
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0140.010
Bibliometrics0.0230.029
Science and technology studies0.0020.003
Scholarly communication0.0060.006
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.516
GPT teacher head0.596
Teacher spread0.080 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainMethods
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

Citations0
Published2021
Admission routes1
Has abstractyes

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