MétaCan
Menu
Back to cohort
Record W4381095243 · doi:10.2196/44747

Understanding the Role of Patient Portals in Fostering Interprofessional Collaboration Within Mental Health Care Settings: Mixed Methods Study

2023· article· en· W4381095243 on OpenAlexafffundvenueabout
Keri Durocher, Hwayeon Danielle Shin, Brian Lo, Sheng Chen, Clement Ma, Gillian Strudwick

Bibliographic record

VenueJMIR Human Factors · 2023
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of TorontoLambton CollegeCentre for Addiction and Mental HealthWestern University
FundersCanadian Nurses Foundation
KeywordsPatient portalMental healthMedicineEmpowermentNursingHealth careQualitative researchPsychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Patient portals are web-based systems through which patients can access their personal health information and communicate with their clinicians. The integration of patient portals into mental health care settings has been evolving over the past decade, as cumulated research to date has highlighted the potential role of portals in facilitating positive health outcomes. However, it is currently unknown whether portal use can foster interprofessional collaboration between clinicians and patients or whether the portal is a tool to support an already established collaborative relationship. OBJECTIVE: This mixed methods study aimed to understand how the use of a patient portal within mental health settings can impact the level of interprofessional collaboration between clinicians and patients. METHODS: This study was conducted in a large mental health care organization in Ontario, Canada. A convergent mixed methods design was used, where the primary data collection methods included questionnaires and semistructured interviews with patients who had experience using a portal for their mental health care. For the quantitative strand, participants completed the Health Care Communication Questionnaire and the Self-Empowerment subscale of the Mental Health Recovery Measure at 3 time points (baseline, 3 months of use, and 6 months of use) to measure changes in scores over time. For the qualitative strand, semistructured interviews were conducted at the 3-month time point to assess the elements of interprofessional collaboration associated with the portal. RESULTS: For the quantitative strand, 113 participants completed the questionnaire. For the Health Care Communication Questionnaire scores, the raw means of the total scores at the 3 time points were as follows: baseline, 43.01 (SD 7.28); three months, 43.19 (SD 6.65); and 6 months, 42.74 (SD 6.84). In the univariate model with time as the only independent variable, the scores did not differ significantly across the 3 time points (P=.70). For the Mental Health Recovery Measure scores, the raw mean total scores at the 3 time points were as follows: baseline, 10.77 (SD 3.63); three months, 11.09 (SD 3.81); and 6 months, 11.10 (SD 3.33). In the univariate model with time as the only independent variable, the scores did not differ significantly across the 3 time points (P=.34). For the qualitative strand, 10 participants were interviewed and identified various elements of how interprofessional collaboration can be supplemented through the use of a patient portal, including improved team functioning, communication, and conflict resolution. CONCLUSIONS: Although the quantitative data produced nonsignificant findings in interprofessional collaboration scores over time, the patients' narrative accounts described how the portal can support various interprofessional collaboration concepts, such as communication, leadership, and conflict resolution. This provides useful information for clinicians to support the interprofessional relationship when using a portal within a mental health setting. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2018-025508.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.132
GPT teacher head0.515
Teacher spread0.383 · 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 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

Citations11
Published2023
Admission routes4
Has abstractyes

Explore more

Same venueJMIR Human FactorsSame topicElectronic Health Records SystemsFrench-language works237,207