Nurse <scp>PATHIT</scp> : An Adapted Framework for Nurse Support for Patient Activation Through Health Information Technology
Bibliographic record
Abstract
AIMS: This article presents an adapted framework that integrates the Patient Health Engagement (PHE) model with Orem's Nursing Systems theory. The framework highlights the nursing role in encouraging Health Information Technology (HIT) tools, such as secure messaging and patient portals, to enhance patient activation and support their self-care capabilities, particularly in chronic disease management aided by nursing actions. BACKGROUND: Despite HIT's role in improving patient care and the increased government incentives for its adoption, utilisation remains low due to various sociodemographic factors and psychosocial factors. The nursing discipline addresses these factors in its practice and thus could facilitate the use of HIT-related tools for patients. We propose an adapted framework embedded in nursing priorities for building self-care agencies in patients centred around HIT use. METHODS: We aligned Orem's nursing system model and PHE model to propose an adapted Nurse PATHIT framework that provides actions and considerations for nursing discipline to target the HIT tools for enhancement of patient activation in chronic care engagement based on patient's readiness. RESULTS: The integration of the PHE model with Nursing System theory offers a framework for promoting HIT use as one of the tools for managing chronic diseases by building self-care agency in patients. Three of the four stages of the PHE model-blackout, arousal and adhesion-correspond to the wholly compensatory, partially compensatory and supportive approaches within Nursing Systems theory. CONCLUSION: Adapting the PHE model with Orem's Nursing System theory in the form of Nurse PATHIT creates a comprehensive framework for nurses to encourage the use of HIT tools in chronic disease management. Future research on patient-centred outcomes using HIT can test this framework by incorporating support and motivation for HIT use for patients as one of the tools to actively engage patients in nursing care planning to increase patients' self-care agency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".