The impact of mobile health monitoring on the evolution of patient-pharmacist relationships
Bibliographic record
Abstract
Purpose Increasingly popular mobile health technology is creating a new paradigm for the delivery of care to patients involving a role of the pharmacist. This study aims to propose a renewed patient–pharmacist relationship in this environment and present an empirical case study investigating the influence of key variables, including the consumer’s attitude toward personalized monitoring performed by the pharmacist, on the intention to adopt a mobile health app. Other drivers identified were ease of use and perceived usefulness of the app, individual and health-related factors (perceived vulnerability and severity of health condition, social norms and innovativeness with technology) and quality of relationship with the pharmacist. Design/methodology/approach A self-administered online survey was completed by 356 Canadian mobile device owners of more than 40 of age. Analyses were performed using structural equation modeling. Findings The main factor driving adoption intentions was perceived usefulness followed by the respondent’s innovativeness with technology and perceived vulnerability of his/her health condition. Attitude toward personalized monitoring depends primarily on the relationship with the pharmacist. No relationship was found between adoption intentions and attitude toward personalized monitoring. Originality/value This research features a multidisciplinary approach by using variables from relational marketing, information technology and health and inclusion of the pharmacist (vs physician) as a health consultant, offering relevant marketing avenues for pharmacists.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".