Barriers to Knowledge Management Practices, Interprofessional Collaboration and Information Technology Application in Federal Tertiary Hospitals in Nigeria
Bibliographic record
Abstract
Effective dissemination of knowledge among healthcare professionals using information and telecommunication technology has been identified as an important tools to improve qaulity service delivery. This study provide succinct explanation of the constraints of using information technology in the management of knowledge among diverse healthcare professionals in federal tertiary hospitals in Nigeria. The study specifically focus on factors militating against the effective use of information technology application to manage knowledge to promote interprofessional collaboration for improved quality service deleivery. Cross-sectional data were collected from 479 healthcare workers across the federal tertiary hospitals in Nigeria. Using the Relative Importance Index (RII), the results show that inadequate ICT infrastructure (R = 0.79), inadequate technical support (R = 0.78), ICT illiteracy (R = 0.77), inadequate management support (R= 0.76), behavioural and personal characteristics (R = 0.72), among others are the major barriers militating against interprofessional collaboration and knowledge management in federal tertiary hospitals in Nigeria. Majority of the respondents also perceived lack of time and interaction among healthcare workers, poor verbal communication, level of experience, organisational structure and hierarchy, difference in education and gender as parts of the sociocultural factors inhibiting effective management of knowledge and interprofessional collaboration among healthcare workers. The study concludes that provision of basic information and communication technology facility to healthcare workers is paramount to enhance knowledge sharing and interprofessional collaboration to improve quality health service delivery. Adequate funding, provision of medical infrastructure and basic amenities, healthcare workers eduction and orientation towards the benefits of interprofessional collaboration using the IT application are suggested as ways to improve quality healthcare services in tertiary hospiatls.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".