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Record W2886445435 · doi:10.1186/s12913-018-3418-x

Teledermatology scale-up frameworks: a structured review and critique

2018· review· en· W2886445435 on OpenAlexaff
Laticha Elizabeth Marolana Walters, Richard E. Scott, Maurice Mars

Bibliographic record

VenueBMC Health Services Research · 2018
Typereview
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsUniversity of Calgary
FundersFogarty International CenterNational Institutes of HealthInyuvesi Yakwazulu-NataliCouncil for Scientific and Industrial Research, South Africa
KeywordsTeledermatologyScale (ratio)eHealthTelemedicineTelehealthHealth informaticsMedicinePublic healthPublic sectorNursing researchHealth administrationNursingHealth careKnowledge managementComputer scienceGeography

Abstract

fetched live from OpenAlex

BACKGROUND: The South African public health sector embarked on a National Telemedicine System implementation program in 1999 and although unsuccessful, the Province of KwaZulu-Natal subsequently implemented teledermatology in 2003, with two currently active services (synchronous and asynchronous). Although sustained these have not been scaled-up to meet the needs of all hospitals in the Province. A recent teledermatology scale-up design requirements elicitation process within KwaZulu-Natal confirmed the need for a framework, and identified requirements through key stakeholders, programme observations, the literature, and experts. This study aimed to identify and characterise existing teledermatology or related eHealth scale-up frameworks, determine whether any met the previously elicited scale-up framework requirements, and were suitable for use in the KwaZulu-Natal public health sector. METHODS: A structured literature search was performed of electronic databases (Scopus, Science Direct, IEEE, PubMed, and Google Scholar) seeking proposed or developed teledermatology or related scale-up frameworks. Global public health publications were also hand-searched. The teledermatology or telemedicine, telehealth or eHealth related scale-up frameworks identified were critiqued against the previously elicited teledermatology scale-up framework requirements to determine their suitability for use. RESULTS: No specific teledermatology scale-up framework was found. Seven related scale-up frameworks were identified, although none met all the previously identified teledermatology scale-up framework requirements. The identified frameworks were designed for specific scale-up phases and lacked a more holistic and comprehensive approach. CONCLUSIONS: There is an evidenced-based need for the development of a health sector aligned, holistic framework that meets the identified teledermatology scale-up framework requirements. The findings of this paper will inform development of such a framework.

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)0.120
metaresearch head score (Gemma)0.217
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.120
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.217
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0360.028
Science and technology studies0.0030.005
Scholarly communication0.0080.008
Open science0.0070.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.068
GPT teacher head0.484
Teacher spread0.416 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2018
Admission routes1
Has abstractyes

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