MHealth and perceived quality of care delivery: a conceptual model and validation
Bibliographic record
Abstract
BACKGROUND: The objective of this research is to examine, conceptualize, and empirically validate a model of mobile health (mHealth) impacts on physicians' perceived quality of care delivery (PQoC). METHODS: Observational quasi-experimental one group posttest-only design was implemented through the empirical testing of the conceptual model with nine hypotheses related to the association of task and technology characteristics, self-efficacy, m-health utilization, task-technology fit (TTF), and their relationships with PQoC. Primary data was collected over a four-month period from acute care physicians in The Ottawa Hospital, Ontario, Canada. The self-reported data was collected by employing a survey and distributed through the internal hospital channels to physicians who adopted iPads for their daily activities. RESULTS: Physicians' PQoC was found to be positively affected by the level of mHealth utilization and TTF, while the magnitude of the TTF direct effect was two times stronger than utilization. Additionally, self-efficacy has the highest direct and total effect on mHealth utilization; in the formation of TTF, technological characteristics dominate followed by task characteristics. CONCLUSION: To date, the impact of utilized mHealth on PQoC has neither been richly theorized nor explored in depth. We address this gap in existing literature. Realizing how an organization can improve TTF will lead to better PQoC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".