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Record W2746005459 · doi:10.1136/eb-2017-102775

Mobile phone messaging delivering encouragement, reminders and education increases patient compliance with recommended exercise and results in positive short-term health behaviours

2017· letter· en· W2746005459 on OpenAlexaff
Christina Hurlock‐Chorostecki

Bibliographic record

VenueEvidence-Based Nursing · 2017
Typeletter
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsMobile phoneCompliance (psychology)Term (time)Text messagingPhonePsychologymHealthPatient complianceApplied psychologyMedicineMultimediaInternet privacyComputer scienceNursingFamily medicineSocial psychologyPsychological intervention

Abstract

fetched live from OpenAlex

Commentary on:  Chen H, Chuang T, Lin P, et al . Effects of messages delivered by mobile phone on increasing compliance with shoulder exercises among patients with a frozen shoulder. J of Nursing Scholarship 2017; 49 :429–37. Mobile phone communication is growing rapidly across the globe with 7.6 billion subscriptions reported worldwide in 2017.1 There is quality evidence supporting the use of mobile phone communication by healthcare 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.086
GPT teacher head0.424
Teacher spread0.337 · 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.

Study designSystematic review
Domainnot available
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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