Digital Education for the Management of Chronic Wounds in Health Care Professionals: Protocol for a Systematic Review by the Digital Health Education Collaboration
Bibliographic record
Abstract
BACKGROUND: Digital education is "the act of teaching and learning by means of digital technologies." Digital education comprises a wide range of interventions that can be broadly divided into offline digital education, online digital education, digital game-based learning, massive open online courses (MOOCs), psychomotor skills trainers, virtual reality environments, virtual patient simulations, and m-learning. Chronic wounds pose an immense economic and psychosocial burden to patients and the health care system, as caring for them require highly specialized personnel. Current training strategies face significant barriers, such as lack of time due to work commitments, distance from provider centers, and costs. Therefore, there is an increased need to synthesize evidence on the effectiveness of digital education interventions on chronic wounds management in health care professionals. OBJECTIVE: Our main objective is to assess the effectiveness of digital education as a stand-alone approach or as part of a blended-learning approach in improving pre- and postregistration health care professionals' knowledge, attitudes, practical skills, and behavior in the management of chronic wounds, as well as their satisfaction with the intervention. Secondary objectives are to evaluate patient-related outcomes, cost-effectiveness of the interventions, and any unfavorable or undesirable outcomes that may arise. METHODS: This systematic review will follow the methodology as described in the Cochrane Handbook for Systematic Reviews of Interventions. As our systematic review is one of a series of reviews on digital education for health professionals' education, we will use a previously developed search strategy. This search includes the following databases: the Cochrane Central Register of Controlled Trials (CENTRAL) (Cochrane Library), MEDLINE (Ovid), Embase (Ovid), Web of Science, the Educational Resource Information Centre (ERIC) (Ovid), PsycINFO (Ovid), the Cumulative Index to Nursing and Allied Health Literature (CINAHL) (EBSCO), the ProQuest Dissertation and Theses database, and trial registries. Databases will be searched for studies published from January 1990 to August 2018. Two independent reviewers will screen the library for included studies. We will describe the screening process using a flowchart as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We will extract the data using a previously developed, structured data extraction form. Included studies will be quality-assessed using the Risk of Bias tool from Cochrane. We will narratively summarize the data and, if possible, we will conduct a meta-analysis. We will use Cochrane's RevMan 5.3 software for data analysis. RESULTS: We have completed the screening of titles and abstracts for this systematic review and are currently selecting papers against our inclusion and exclusion criteria through full-text revision. We are expecting to complete our review by the end of April 2019. CONCLUSIONS: This systematic review will provide an in-depth analysis of digital education strategies to train health care providers in the management of chronic wounds. We consider this topic particularly relevant given the current challenges facing health care systems worldwide, including shortages of skilled personnel and a steep increase in the population of older adults as a result of a prolonged life expectancy. TRIAL REGISTRATION: PROSPERO CRD42018109971; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=109971. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12488.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".