Oncology Training Needs Assessment Among Health Care Professionals in Nigeria
Bibliographic record
Abstract
PURPOSE This study investigated the status of training and preparedness for oncology practice and research and degree of interprofessional collaboration among health care professionals in the six geopolitical regions of Nigeria. METHODS A convergent parallel mixed methods design was used. Three hundred seventeen respondents completed a three-part, online questionnaire. Self-rated competencies in oncology research (26 items), oncology practice (16 items), and interprofessional collaboration (nine items) were assessed with a one- to five-point Likert scale. Six key informant and 24 in-depth interviews were conducted. Descriptive statistics, analysis of variance, and pairwise t-test were used to analyze the quantitative data, whereas thematic analysis was used for the qualitative data. RESULTS Respondents were mostly female (65.6%) with a mean age of 40.5 ± 8.3 years. Respondents include 178 nurses (56.2%), 93 medical doctors (29.3%), and 46 pharmacists (14.5%). Self-assessed competencies in oncology practice differed significantly across the three groups of health care professionals ( F = 4.789, P = .009). However, there was no significant difference across professions for competency in oncology research ( F = 1.256, P = .286) and interprofessional collaboration ( F = 1.120, P = .327). The majority of respondents (267, 82.4%) felt that educational opportunities in oncology-associated research in the country are inadequate and that this has implications for practice. Key training gaps reported include poor preparedness in data analysis and bioinformatics (138, 43.5%), writing clinical trials (119, 37.5%), and writing grant/research proposals (105, 33.1%). Challenges contributing to gaps in cancer research include few trained oncology specialists, low funding for research, and inadequate interprofessional collaboration. CONCLUSION This study highlights gaps in oncology training and practice and an urgent need for interventions to enhance interprofessional training to improve quality of cancer care in Nigeria. These would accelerate progress toward strengthening the health care system and reducing global disparities in cancer outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".