Clinical Nurse Specialists’ Perceptions of a Mental Health Patient Portal
Bibliographic record
Abstract
PURPOSE: The purposes of this study were to explore clinical nurse specialists' views of the potential influence of a mental health portal on nursing practice and to identify portal implementation strategies. METHODS: A qualitative descriptive approach was used. Semistructured interviews were conducted with 5 clinical nurse specialists. Two independent coders conducted an inductive content analysis of the transcribed interviews to generate codes describing patterns in the data to identify originating themes. RESULTS: The content analysis uncovered the following 4 themes: (1) implementation strategies, (2) nurse likelihood to recommend, (3) impact on nursing practice, and (4) perceived influence on patients. CONCLUSION: Direct care nurses may benefit from education and coaching on how to document in the record using patient-centered language that is understandable to patients who may be reading it. In addition, the use of patient portals should be designed to fit into nurses' existing clinical workflows. Finally, more research is needed to identify the benefits and unintended consequences of patient portals within a mental health context.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".