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Record W3036937823 · doi:10.2196/16714

Delivering Clinical Skin Examination Education to Nurse Practitioners Using an Internet-Based, Microlearning Approach: Development and Feasibility of a Video Intervention

2020· article· en· W3036937823 on OpenAlexvenueno aff
Delaney Stratton, Kimberly Shea, Elizabeth P. Knight, Lois J. Loescher

Bibliographic record

VenueJMIR Dermatology · 2020
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityCLARITYIntervention (counseling)MedicineComprehensionMultimediaMedical educationMedical physicsNursingComputer science

Abstract

fetched live from OpenAlex

Background Skin cancer is the most common cancer; survival of the most serious skin cancers and malignant melanomas depends on early detection. Early detection relies on accessibility to clinical skin examination (CSE). Primary care nurse practitioners (PCNPs) are well-positioned to conduct CSEs; however, they require further education on CSE and have time constraints for continuing education. A digitally delivered intervention grounded in microlearning is a promising approach to deliver new information over a brief period. Objective Our objective was to develop and explore the feasibility of implementing a 1-week digital video intervention with content on CSE skills, defined as melanoma risk assessment, head-to-toe skin examination, and pigmented lesion assessment, for PCNPs. Specific aims were as follows: (1) Aim 1: to develop three microlearning-based melanoma videos with content on CSE that are suitable for digital delivery to PCNPs in various formats and (2) Aim 2: to assess the feasibility of the video intervention, including enrollment and retention rates, adherence, and acceptability and usability of the video intervention. Methods For Aim 1, the research team created storyboards for videos that addressed each CSE skill. An expert panel of three dermatologists reviewed the storyboards and videos for relevance, comprehension, and clarity using the content validity index (CVI). The panel evaluated the usability of the video intervention delivery by Research Electronic Data Capture (REDCap) and Vimeo using the System Usability Scale (SUS) and technical video production using Beaudin and Quick’s Quality Evaluation of Video (QEV). Aim 2 evaluated enrollment and retention rates of PCNPs, based on metrics from previous studies of CSE in the literature, and video intervention adherence. SUS and the Attitudes toward Web-based Continuing Learning Survey (AWCL) assessed usability and acceptability. Results CVI scores indicated relevance and clarity for each video: mean scores ranged from 3.79 to 4, where 4 indicated the video was highly relevant and very clear. The integration of REDCap and Vimeo was usable: the SUS score was 96, where 0 was the worst and 100 was the best. The digital delivery of the videos was rated as exceptional on all five technical items: the mean score was 5, where scores ranged from 1 (poor) to 5 (exceptional). Of the 32 PCNPs who were sent emails, 12 enrolled (38%) and, out of these 12, 10 (83%) completed the intervention and the surveys. Video intervention adherence was ≤50%. Participants rated the usability as better (mean 85.8, SD 10.6; better=70-90) and favorably ranked the acceptability of the AWCL’s constructs of perceived usefulness (mean 5.26, SD 0.08), perceived ease of use (mean 5.40, SD 0.41), behavior (mean 5.53, SD 0.12), and affection (mean 5.77, SD 0.04), where scores ranged from 1 (strongly disagree) to 7 (strongly agree). Conclusions The video intervention was feasible to deliver to PCNPs using a digital, microlearning approach. The findings provide support for using the videos as an intervention in a future pilot randomized trial targeting behavioral CSE outcomes among PCNPs and other primary care providers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.076
GPT teacher head0.379
Teacher spread0.303 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2020
Admission routes1
Has abstractyes

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