Personal Health Tracking: A Paradigm Shift in the Self-Care Models in Nursing
Bibliographic record
Abstract
The rapidly evolving digital health landscape necessitates updates to existing self-care models in nursing. This viewpoint paper revisits and evaluates prevalent models, recognizing their comprehensive exploration of self-care concepts while also identifying a gap in the incorporation of personal informatics. It underscores the missing link of human-technology interplay, an essential aspect in understanding self-care practices within digital generations. The author delineates the role of personal health tracking in self-care and the achievement of desired health outcomes. Based on these insights, the author proposes a refined, digitized self-care model that incorporates mobile health (mHealth) technologies and self-tracking behaviors. The paper concludes by advocating the application of this model for future mHealth nursing interventions, providing a framework for facilitating patient self-care and improving health and well-being in the era of digital health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".