Evaluation of a Health Care Worker Training Intervention to Improve the Early Diagnosis and Referral of Childhood Cancers in Ghana: A Qualitative Descriptive Study
Bibliographic record
Abstract
PURPOSE: This study sought to (1) evaluate the perceived effectiveness of an early childhood cancer warning signs and symptoms (EWSS) training intervention on health care worker (HCW) knowledge, attitudes, and clinical practice; (2) evaluate the ease of implementation of training received, including potential barriers and facilitators; and (3) provide insights into program improvements for future iterations of the intervention. METHOD: Using a qualitative descriptive study design, we conducted in-depth, semistructured interviews with 23 purposively sampled Ghanaian HCW recipients of the EWSS training intervention. We undertook iterative thematic analysis of data concurrently with interviews and used a modified version of the theoretical framework of acceptability to guide the evaluation of the training intervention. RESULTS: We identified six themes-affective attitude, burden, intervention coherence, perceived effectiveness, self-efficacy, and quality improvement-that structure participant perceptions of the effectiveness of the EWSS training. Participants generally had a positive attitude to the training intervention, found the content relatively easy to understand, and communicated the positive impacts of the training on their day-to-day practice. However, they also identified patient- and system-level challenges to the real-world implementation of intervention components, including patients' cultural and religious beliefs about illnesses, patients' financial constraints, and inadequately funded health systems. CONCLUSION: Our findings suggest that although an HCW-focused training intervention has the potential to improve timely diagnosis and referral for childhood cancers in Ghana and comparable health system contexts, complementary interventions to address patient- and system-level implementation challenges are required to translate improvements in HCW knowledge to sustained impact on health outcomes for children with cancer.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".