Healthcare Providers' Perspectives on the Implementation of Patient-Centered Care Models in Hospitals
Bibliographic record
Abstract
This study explores healthcare providers' perspectives on the implementation of patient-centered care (PCC) models in hospitals. It aims to identify key themes related to understanding, experiences, perceived benefits, and challenges associated with PCC to inform better practices and policies. A qualitative research design was employed, utilizing semi-structured interviews to collect data from 21 healthcare providers, including doctors, nurses, and administrative staff, in various hospitals. Participants were selected through purposive sampling to ensure diverse perspectives. Data collection continued until theoretical saturation was achieved. The interview data were transcribed and analyzed using NVivo software, following a thematic analysis approach to identify and refine key themes. The analysis revealed four main themes: understanding of PCC, experiences with implementation, perceived benefits, and challenges faced. Providers described PCC as holistic, individualized care requiring effective communication and cultural competence. Implementation experiences highlighted the importance of planning, resource allocation, interdisciplinary collaboration, and feedback mechanisms. Benefits of PCC included improved patient satisfaction and outcomes, better work environments, efficiency in care delivery, patient trust, and enhanced staff morale. Challenges encompassed resistance to change, resource limitations, training and support deficiencies, interpersonal dynamics, and systemic barriers. The study underscores the critical role of comprehensive training, effective communication, and adequate resources in implementing PCC. Despite significant challenges, the benefits of PCC for patients and providers are evident. Addressing implementation barriers through continuous education, supportive policies, and robust feedback mechanisms can enhance the adoption and sustainability of PCC models. Future research should focus on expanding sample diversity, quantitative measures, and the impact of technology on PCC.
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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 | 0.045 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".