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Record W4200264893 · doi:10.3233/shti210724

Perceptions of Personal Health Records in Rural Primary Health Clinics in Canada – Patient Perspectives

2021· book-chapter· en· W4200264893 on OpenAlexaffabout

Bibliographic record

VenueStudies in health technology and informatics · 2021
Typebook-chapter
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsBC Children's HospitalOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsPerceptionHealth careFocus groupPrimary careRural healthHealth recordsMedical recordFocus (optics)Primary health care

Abstract

fetched live from OpenAlex

Personal health records are increasingly being deployed in healthcare settings. In this study we explored patients' perceptions of personal health records in a rural community in Canada where a primary health network is being deployed. A focus group was held and data were thematically analysed. All patients used technology on a regular basis. Themes included communication and information sharing, issues with access to prior health records, data content and data control and features and functions for continuity of care. Participants expressed desire to be owners of their own record, but described instances where they might be too ill to do so. Participants were hopeful that the functions of a personal health record might help to overcome frustrations with current fragmented information and open to using technologies as part of their care process. Personal health records are promising technologies to overcome fragmented care in rural communities.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.004
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.051
GPT teacher head0.411
Teacher spread0.360 · 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 designQualitative
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

Citations0
Published2021
Admission routes2
Has abstractyes

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