Exploring the application of the biopsychosocial model across hospitals in Nigeria: A mixed methods study
Bibliographic record
Abstract
Background: Healthcare delivery models can have significant influence on patient-centred communication due to their influence on the thinking and behaviour of health workers about what constitute sickness and healthcare. While the extant knowledge shows many efforts at transforming Nigeria’s health sector towards sustainability, there are no clear facts on improvements in clinical communication, especially, the adoption of the biopsychosocial model (BPSM) for patient-centred communication (PCC) in the country. Aim: Researchers investigated the adoption of the BPSM for PCC in General Hospitals in Benue State. Methodology: Researchers adopted the pragmatic approach and convergent mixed methods design involving personal survey for 372 patients and in-depth interview for 67 clinicians from 21 of the 23 General Hospitals in the State. Results: Findings show that the BPSM has not been adequately adopted in clinical interactions, consequently, clinical communication is not patient-centred enough. Data analysis using the PCC model shows reasonable evidence of application of the BPSM in some areas, however, the contents of clinical communication largely fall short of the psychosocial and patient involvement characteristics. Inadequate adoption of the BPSM is linked to poor patient satisfaction with the usefulness of clinical interactions for patients’ psychosocial needs and home management of their conditions. Findings indicate a prevalence of systemic issues in the General Hospitals in Benue State interfering with the adoption of the BPSM for PCC. Researchers suggest a purposeful policy direction to enshrine the modern global philosophy of healthcare both in medical practice and education; a strong political will, and a commitment to proper staffing for enhancing the full integration of the BPSM, improving the PCC and clinical communication experience for both patients and clinicians.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".