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Record W2941699886 · doi:10.3233/978-1-61499-951-5-266

Patient and Family Member Readiness, Needs, and Perceptions of a Mental Health Patient Portal: A Mixed Methods Study

2019· article· en· W2941699886 on OpenAlexaffabout
Moshe Sakal, Madison Friesen, Gillian Strudwick

Bibliographic record

VenueStudies in health technology and informatics · 2019
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of British ColumbiaCentre for Addiction and Mental Health
Fundersnot available
KeywordsPatient portalMental healthPerceptionPhoneMedicineNursingFamily medicineHealth carePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Patient portals are a form of technology that supports patients in accessing their health information, and other functions like scheduling appointments. The presence and utilization of patient portals in mental health contexts is relatively new. Despite research existing in the mental health literature that indicates that there may be benefits when patients have access to their mental health notes, there is limited information as to how best to implement portals, and support adoption among patients and their family members. Given this gap in literature, this study aimed to identify patient and family readiness, needs, and perceptions of a mental health patient portal. Surveys were administered to patients (n = 103) and family members (n = 7) either in-person or over the phone by a Peer Support Worker. The sample of participants consisted of patients and family members affiliated with Canada's largest mental health hospital located in Toronto, Ontario. Study results indicated that patients had the highest interest in the following portal functions: scheduling appointments, checking appointment times, and accessing their health record. Both patients and family members expressed their concerns about cybersecurity and potential privacy breaches. The results of this study, as well as the approach, can inform future patient portal planning and implementation activities at other healthcare organizations.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.052
GPT teacher head0.471
Teacher spread0.419 · 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.

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

Citations14
Published2019
Admission routes2
Has abstractyes

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