An Overview on Adopting Clinical Pathways in Primary Care: A Literature Review
Bibliographic record
Abstract
Abstract Primary care is considered an essential service within the public healthcare system because it is responsible for offering continuity of care, patient-centeredness, coordination of care, prevention, health promotion, and patient autonomy. ** Clinicians are increasingly interested in utilizing clinical pathways to improve primary care quality and service delivery, providing more efficient use of available resources. ** At the secondary care level, clinical pathways are regularly used to ensure adequate management of care for a specific population to receive appropriate and timely care in an institution. It would be interesting to know if this process could have the same benefits within primary care. This literature review aims to screen the scientific literature on whether there have been studies able to address the application of clinical pathways in primary care. After screening both titles and abstracts, 26 papers were selected among 5,807 articles found. After the application of inclusion criteria for this literature review, 10 articles were selected. The results indicate the benefits and the limitation of using clinical pathways. However, the lack of resources in primary care and the resistance to change may limit the adoption of CPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".