The relationship between physician visits and some quality of life indicators
Bibliographic record
Abstract
Background: A patient survey and chart review were conducted to determine if there is a relationship between the number of visits a patient makes to a family physician and eight quality of life indicators: selfrated health, self-rated stress, selfrating of health care received, satisfaction with life as a whole, satisfaction with health, spiritual fulfillment, overall quality of life, and happiness. Methods: The survey required respondents to identify their level of concern or satisfaction with quality of life indicators using a Likert scale. Respondents consisted of adults (age 17 and older) living in British Columbia’s Bella Coola Valley and attending the Bella Coola Medical Clinic. After respondents completed the survey, their charts were reviewed to determine the number of visits they made to family physicians. Data obtained from the survey answers were combined with data obtained from reviewing the charts. The relationships revealed by the two sets of data were then considered. Results: An estimated 1734 Bella Coola residents were deemed eligible to complete the quality of life survey. A total of 968 usable surveys were returned, for a response rate of 56% (968 of 1734). One-way ANOVA testing revealed there is a relationship between the number of visits to a physician and the scores for selfrated health (P<0.001) and stress (P≤0.001), satisfaction with life (P<0.001) and health (P<0.001), spiritual fulfillment (P=0.002), overall quality of life (P<0.001), and happiness (P<0.001). No relationship was found between the number of visits to a physician and the respondents’ rating of health care received (P=0.127). Conclusions: There is a relationship between the number of times a person visits a family physician and his or her self-rated health and stress, satisfaction with life and health, spiritual fulfillment, overall quality of life, and happiness. More visits to a physician were associated with greater dissatisfaction with life. Better understanding of these relationships may lead to strategies designed to reduce the number of visits to physicians. Background A common theme emerging from the many discussions, commissions, and inquiries about Canada’s health care system is that “better management is required, not more money.”
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".