Users’ attitudes towards personal health records
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Prevention and management of chronic conditions is a priority for many healthcare systems. Personal health records have been suggested to facilitate implementation of chronic care programs. However, patients' attitude towards personal health records (PHRs) can significantly affect the adoption rates and use of PHRs. OBJECTIVES: to evaluate the attitude of patients with Type II diabetes towards using a PHR to manage their condition. METHODS: We used a cross-sectional exploratory pilot study. Fifty-four (54) patients used a PHR to monitor and record their blood glucose levels, diet, and activities for 30 days, and to communicate with their clinicians. At the end of the study, patients responded to a survey based on three constructs borrowed from different technology acceptance frameworks: relative advantage, job fit, and perceived usefulness. A multivariate predictive model was formed using partial least squaring technique (PLS) and the effect of each construct on the patients' attitude towards system use was evaluated. Patients also participated in a semi-structured interview. RESULTS: We found a significant positive correlation between job fit and attitude (JF → ATT = +0.318, p<0.01). There was no statistical evidence of any moderating or mediating effect of other main constructs or any of the confounding factors (i.e., age, gender, time after diagnosed) on attitude. CONCLUSION: The attitude of patients towards using PHR in management of their diabetes was positive. Their attitude was mainly influenced by the extent to which the system helped them better perform activities and self-manage their condition.
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.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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 it