Knowledge, attitude, and practices of stakeholders involved in healthcare financing programs on economic evaluations in Cameroon
Bibliographic record
Abstract
There are many healthcare financing programs (HFPs) in Cameroon; however, there is a lack of information on these programs' economic effectiveness and efficiency. Involvement of local stakeholders in the economic evaluations (EEs) of HFPs is critical for ensuring contextual factors are considered prior to program implementation. We conducted a cross-sectional study to assess the need for EEs of Cameroonian HFPs. Regular staff in supervisory roles aged 18 years and above were recruited in four Cameroonian cities. Data were collected via face-to-face surveys between June 15 and August 1, 2022. Descriptive analyses summarized participants' knowledge, attitudes, and practices in relation to performing EEs of HFPs. Principal component analyses identified organizational, individual, and contextual factors that could influence participants' involvement. The total sample included 106 participants. On average, 65% of participants reported being aware of the listed HFPs; however, of these, only 28% said that they had been involved in the HFPs. Of the 106 participants, 57.5% knew about EEs; yet, almost 90% reported that the HFP in question had never been subject to an EE, and 84% had never been involved in an EE. Most participants indicated that they had intended or would like to receive EE training. Using principal component analyses, the organizational factors were classified into two components ('policy and governance' and 'planning and implementation'), the individual factors were classified into two components ('training' and 'motivation'), and the contextual factors were classified into three components ('funding,' 'political economy,' and 'public expectations'). The findings of this study highlight the need to invest in EE training to improve participation rates of Cameroonian stakeholders in the EEs of HFPs. Improved knowledge, diversified skills, and increased participation of stakeholders from all levels of the Cameroonian healthcare system are critical to the effective and efficient development, implementation, and EE of the country's HFPs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".