Exploring Communication Processes in Referral Pathways for Chronic Disease Management: Malaysian Public Primary Health care Experiences
Bibliographic record
Abstract
BACKGROUND: Referrals are a two-way communication between healthcare facilities to enable information transfer for the continuity of patient care. The Enhanced Primary Healthcare (EnPHC) initiative, a complex intervention package to improve non-communicable disease (NCD) management, introduced improvements to the NCD’s referral mechanism from primary healthcare clinics to the hospital. This study explores the communication process between the Malaysian public primary healthcare and hospital for chronic care management. METHOD: A qualitative exploratory study using purposive sampling was done in all twenty EnPHC intervention clinics. In-depth interviews and focus group discussions were carried out among all healthcare providers working in EnPHC clinics. The 47 interview sessions were audio-recorded, transcribed verbatim, and analyzed thematically. RESULTS: A total of 97 healthcare providers participated. Three main themes of the communication process between the primary health care and hospital during the implementation of EnPHC intervention emerged from the analysis. These are; (1) structured information relay, (2) no show appointment tracking via various communication devices has strengthened the mechanism to monitor the referred patient appointment scheduling and their adherence to the appointment, and (3) inter-facility networking facilitated the implementation of EnPHC’s referral mechanism. CONCLUSION: The EnPHC referral mechanism created a platform for PHC clinics and hospitals to communicate and build rapport to help ensure care continuity for NCD patients. The traditional method of communication between healthcare facilities should change and instead start using the newest or most current, advanced technology.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".