Informational, Physical, and Psychological Privacy as Determinants of Patient Behaviour in Health Care
Bibliographic record
Abstract
This chapter presents and describes a theoretical framework explicating how three dimensions of privacy in healthcare (i.e. informational, physical, and psychological) influence patient behaviour through trust. Informational privacy is defined as the patients' perceptions of the degree of control over their personal information when their doctor collects, uses, disseminates, and stores their information. Physical privacy refers to the patients' perceptions of the degree of their physical inaccessibility to others. Psychological privacy is the patients' perceptions of the extent to which the physician allows them to participate in their healthcare decisions and maintains their personal and cultural values, such as inner thoughts, feelings, cultural beliefs, and religious practices. These types of privacy are especially important with respect to service quality and patient safety due to the recent advancements in information and telecommunication technologies and the availability of online medical information. As a result, patients have become more educated in various health issues, and many of them want to actively participate in their health decisions. The framework proposes that these privacy dimensions affect trust in a healthcare provider. Trust, in turn, has an effect on treatment compliance, positive word-of-mouth, and commitment to stay with the current service provider in the future. Based on the framework, recommendations for healthcare stakeholders are provided.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".