Analyzes of health profiles of patients receiving home health care services over last five years in Diyarbakir, Turkey
Bibliographic record
Abstract
Objective: To determine the health profiles of patients who have been delivered home health care services by public hospitals in over the last five years.Methods: Data which were recorded by the Ministry of Health and the Diyarbakır Directorate of Health Services had been provided from 1,826 patients from three city center and five district hospitals located in Diyarbakir, Turkey.Results: Of 1,826 patients, 989 (54%) were female and 837 (46%) were male. The patients were divided into three age groups: 0-18 years 467 (25%), 19-64 years 511 (28%) and +65 years 848 (47%). There were more female patients in +65 age group and male in 0-18 age group (χ2 = 89.923, p < .001). In 2015, there was an increase in the number of people who received services in all age groups (χ2 = 164.344, p < .001). It was found that neurodegenerative diseases were more widespread in +65 age group (χ2 = 44.823, p < .001). Neurogenetic dieases were more seen in the 0-18 age group (χ2 = 97.800, p < .001). In the +65 age group, musculoskeletal (χ2 = 16.037, p < .001) and respiratory diseases (χ2 = 6.054, p < .05) were detected to be more widespread. There were no significant sex-based differences between the years involved (χ2 = 2.246, p > .05). Neurodegenerative diseases (χ2 = 4.07, p < .05), neurogenetic diseases (χ2 = 6.043, p < .05) and respiratory diseases (χ2 = 5.254, p < .05) were determined to be more widespread in male patients. Musculoskeletal (χ2 = 17.943, p < .001)and cardiovascular diseases (χ2 = 11.994, p < .001) were seen more in female groups.Conclusions: Over the last five years, there has been an increase in the utilization of home health care services. Policy legislation for health care services to be provided at home is generating succesful results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".