Importance of relationships with primary care. Implications for patients and health care.
Bibliographic record
Abstract
Background: Primary care is an essential part of all health care systems, constituting complex networks of relationships.If choice of primary care provider, i.e. active listing is allowed this could be linked to the strength of the relations between patients and primary care.Other aspects of these relationships could be measured using number of consultations.Primary care could be described as a healthcare system, with hospitalisation as an outcome.Objectives: The first aim of this thesis was to describe active listing and the associations with age, sex, multimorbidity, consultations, socioeconomic status and location.The second aim was to study whether relationships between patients and primary care are associated with hospitalisation, when adding patient complexity and investigating differences in performance within primary care.Methods: Cross-sectional population studies in a small Swedish county with 151 731 inhabitants in 2007.Data were collected from patient records, merged with data from Statistics Sweden in Papers II and IV.Active listing was the outcome of logistic regression models in Papers I-II.Hospitalisation, i.e. risk of hospital admission and mean hospital days, was the outcome of zero-inflated negative binomial regression models adjusted for multimorbidity, age and sex in Papers III-IV.Hospitalisation was analysed as an outcome of primary care investigating associations with active listing and number of consultations in primary care.Paper III also investigated psychiatric disorders, and Paper IV also investigated socioeconomic factors and differences in primary care.Results: The results of Paper I showed that number of consultations, multimorbidity level, age and sex were associated with active listing, and that data from primary care explained more of active listing than data from all health care.Paper II showed that multimorbidity, age, geographical location and primary care explained more of active listing than socioeconomic factors and distances to health care.Papers III-IV showed that patients actively listed or with more than one consultation were hospitalised for less than mean (0.9) days while patients passively listed or with 0-1 consultation were hospitalised for more than mean.Paper III showed that in RUB 3, moderate need for care, patients actively listed were in mean hospitalised for 3.45 (95%CI 2.84-4.07)days, if diagnosed with any psychiatric disorder and 1.64 (95%CI 1.50-1.77)days if not.Patients passively listed in RUB 3 were in mean hospitalised for 5.17 (95%CI 4.36-5.98)days, if diagnosed with any psychiatric disorder and 2.41 (95%CI 2.22-2.60)if not.Paper IV established differences within primary care comparing two types of primary care practices.Odds off hospital admission differed 49% and mean days hospitalised 0.24 days.Conclusions: Active listing in primary care is explained more by multimorbidity, age, sex and factors in local society and health care, than socioeconomic status and distances to health care.Good relationships with primary care are associated with less hospitalisation, more so when health care handles more complex multimorbidity, like including psychiatric disorders.Differences in primary care imply that management of primary care could affect hospitalisation.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".