Respect in the Eyes of Non-Urban Elders: Using Qualitative Interviews to Distinguish Community Elders’ Perspective of Respect in General and Healthcare Services
Bibliographic record
Abstract
This study aimed to describe the connotation of respect for community elders in daily situations, and discuss the elderly's views on respect for healthcare services. A qualitative research design was conducted to interview elders from a non-urban area in Changhua, Taiwan. Study sites were Lukang and Ershui. A total of 52 people were interviewed, with an average age of 75 years old. Based on Grounded theory, the thematic analysis method was used to analyze data. This study found that respect from the perspective of the elderly can be divided into three categories: (1) verbal expression, (2) non-verbal behavior, and (3) behavior combined with appropriate language. We found that elders use the performance of healthcare service providers to discuss respect in the field of healthcare services. Respect can also be shown in the physical environment in healthcare settings. This study found that, for the community elders, respect is an individual's subjective feelings regarding the process of interpersonal interaction. Compared to daily life, the respect of the elderly for the healthcare setting has increased the element of the environment. In addition, it was found that elderly people have lower expectations and requirements for respect in healthcare settings.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".