MétaCan
Menu
← Back to cohort
Record W2972809287 · doi:10.3233/shti190782

Promoting Participatory Health: Connecting Nurses and Consumers at Point of Care to Enhance Safety and Quality in Australia

2019· article· en· W2972809287 on OpenAlexaff
Carey Mather, Elizabeth Cummings

Bibliographic record

VenueStudies in health technology and informatics · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordseHealthBusinessContinuanceHealth carePublic relationsNursingQuality (philosophy)Mobile technologyCitizen journalismPatient safetyMarketingMobile deviceMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

Recent research involving representatives from nursing professional organisations found a lack of governance regarding access and use of mobile technology has led to the maintenance of outdated safety and quality strategies. Current organisational policies and guidelines preclude nurses from aligning with the Australian National Safety and Quality in Health Service Standards. Continuance of the mobile technology paradox,where there is theinability of nurses to access and use mobile technology at point of care, hinders the promotion of positive two-way communication between consumers and nurses as the lack of connectivity impedes opportunities for nurses to partner with consumers to promote participation in their own healthcare, develop mutuality of understanding, and improve health and ehealth literacy. Legitimisation ofthe use of mobile technology at point of care is necessaryto supportmeeting consumer expectations, improve the consumer experience and promote participatory health, while contributing to delivery ofcontemporaryhealthcare.

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)0.022
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.010
Scholarly communication0.0050.006
Open science0.0020.016
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.286
GPT teacher head0.539
Teacher spread0.253 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueStudies in health technology and informatics→Same topicMental Health and Patient Involvement→French-language works237,207→