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Record W1994036846 · doi:10.2196/mhealth.4011

Mobile Access to ClinicalConnect: A User Feedback Survey on Usability, Productivity, and Quality

2015· article· en· W1994036846 on OpenAlexafffundvenueabout
Bell Raj Eapen, Barbara Chapman

Bibliographic record

VenueJMIR mhealth and uhealth · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsHamilton Health Sciences
FundersGovernment of CanadaHamilton Health Sciences
KeywordsUsabilityComputer scienceQuality (philosophy)User interfaceMobile appsProductivitymHealthWorld Wide WebHuman–computer interactionMedicinePsychological interventionNursing

Abstract

fetched live from OpenAlex

BACKGROUND: ClinicalConnect, a federated clinical viewer for South West Ontario, Canada, launched a mobile interface in June 2012. OBJECTIVE: The aim of the study was to assess usability of the mobile interface and the perceived impact on productivity of health care providers and quality of healthcare delivery. METHODS: A survey was conducted using the System Usability Scale (SUS) and questionnaires designed to measure productivity and quality based on Canada Health Infoway's Benefits Evaluation framework. RESULTS: The mean SUS score was 67 based on 77 responses. The mean scores for productivity and quality were 3.37 (N=74) and 3.62 (N=71), respectively, on a 5-point Likert scale where 3 was neutral. CONCLUSIONS: Users perceived the mobile interface of ClinicalConnect as useful but were neutral about the ease of use.

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)0.008
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.381
GPT teacher head0.581
Teacher spread0.199 · 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 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

Citations6
Published2015
Admission routes4
Has abstractyes

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