Influence of well-being and quality of work-life on quality of care among healthcare professionals in southwest, Nigeria
Bibliographic record
Abstract
The Nigerian healthcare industry is bedevilled with infrastructural dilapidations and a dysfunctional healthcare system. This study investigated the influence of healthcare professionals' well-being and quality of work-life (QoWL) on the quality of care (QoC) of patients in Nigeria. A multicentre cross-sectional study was conducted at four tertiary healthcare institutions in southwest, Nigeria. Participants' demographic information, well-being, quality of life (QoL), QoWL, and QoC were obtained using four standardised questionnaires. Data were summarised using descriptive statistics. Inferential statistics included Chi-square, Pearson's correlation, independent samples t-test, confirmatory factor analyses and structural equation model. Medical practitioners (n = 609) and nurses (n = 570) constituted 74.6% of all the healthcare professionals with physiotherapists, pharmacists, and medical laboratory scientists constituting 25.4%. The mean (SD) participants' well-being = 71.65% (14.65), QoL = 61.8% (21.31), QoWL = 65.73% (10.52) and QoC = 70.14% (12.77). Participants' QoL had a significant negative correlation with QoC while well-being and quality of work-life had a significant positive correlation with QoC. We concluded that healthcare professionals' well-being and QoWL are important factors that influence the QoC rendered to patients. Healthcare policymakers in Nigeria should ensure improved work-related factors and the well-being of healthcare professionals to ensure good QoC for patients.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".