Perception of Own Illness and Trust in Medical Personnel among Chronically Ill People
Bibliographic record
Abstract
Appropriate perceptions of own disease by chronically ill person significantly affects the success of the diagnostic and therapeutic process. It depends on the existential situation of the patient, the adopted strategy of coping with the disease, received social support, as well as on the way the patient is treated by medical personnel. The aim of the conducted research was to assess the relationship between the perception of the disease by chronically ill people and their trust in medical staff. The study involved 511 people receiving treatment for chronic diseases. The diagnostic survey method was used in the study, the research tools were: the Imagination and Perception of Illness Scale (IPIS), the Brief Illness Perception Questionnaire (Brief IPQ), the Trust in Physician scale by L.A. Anderson and R.F. Dedrick, and a self-authorship questionnaire. Among the studied population, statistically significant relationships were observed between the perception of own disease by the patient, measured with the IPIS scale, and the trust in medical personnel calculated with the Trust in Physician. In the study group, there are statistically significant differences between the belief of the respondents in the effectiveness of treating their own disease and the overall result of trust in medical personnel. The perception of own disease by chronically ill people affects the level of trust in medical staff. The way the patients will perceive their illness depends, among other things, on the relationship between them and the doctor.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".