Women’s experiences of using patient portals in healthcare settings: a rapid review
Bibliographic record
Abstract
BACKGROUND: The integration of patient portals into health systems has the potential to increase access for women. For example, within a mental health context, women are at a higher risk of developing anxiety and depression but may experience barriers in accessing care. Therefore, the aim of this rapid review was to understand what is currently known about patient portal use among women. The objectives were (1) To discover pertinent facilitators for women when using patient portals; (2) To discern if women face individual barriers to accessing patient portals; and (3) To explore the potential role of patient portals for women's mental health care. METHODS: A rapid review methodology was implemented using the Cochrane Rapid Reviews Methods Group, as well as a grey literature scan. Inclusion criteria included articles that focused on women's use of patient portals within healthcare settings. Four databases were searched, including Embase, MEDLINE (Ovid), CINAHL and PsycINFO in September 2023. Two independent reviewers performed screening, data extraction, and analysis. RESULTS: This search resulted in screening 1385 titles and abstracts, and 82 articles for full-text eligibility. 15 articles were included in the review. Data were extracted and analyzed, guided by the research objectives. Facilitators to patient portal use included processes that enhanced user engagement, the provision of portal tools, and management of health care features. Barriers included health equity factors and use of medical jargon. Applications to mental health care included how use of portals eased women's anxiety and increased their sense of internal control. CONCLUSIONS: The results of this review indicate that interrelated factors influence women's experiences with using patient portals. By understanding facilitators and barriers to portal use, and applications for mental health care, we can understand how to improve women's use of portals in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".