Using a multimethod approach to develop implementation strategies for a cervical self-sampling program in Kenya
Bibliographic record
Abstract
BACKGROUND: Numerous health policy makers/researchers are concerned about the limitations of research being applied to support informed decision/policy making and the implementation of practical solutions. The aim of the Chaguo Letu project (which means our choice in Swahili) was to determine how local decision makers could apply a multimethod approach to make strategic decisions to effectively implement a Cervical Self-Sampling Program in Kenya. METHODS: A multimethod approach, involving participatory action research, scenario based planning, and phenomenology, was applied in conjunction with two tools to identify relevant factors (negative or positive) that could impact Cervical Self-Sampling Program implementation. A total of 107 stakeholders participated in interviews, focus groups, workshops, and informal interactions. Content analysis, an affinity exercise, and impact analysis were used to analyze data and develop robust strategic directions and supporting implementation strategies. RESULTS: A total of 57 factors thought to impact the implementation of the Cervical Self-Sampling Program were identified and grouped into 13 thematic categories. These themes were instrumental in developing 10 strategic directions and 22 implementation strategies deemed necessary to implement a technically viable, politically supported, affordable, logistically feasible, socially acceptable, and transformative Program. CONCLUSIONS: This study made three conclusions: 1) there is political will and a desire to improve cervical screening across Kenya, but in a period of dynamic change resources are constrained; 2) implementing the Program in urban/rural settings is logistically feasible, but the majority of Kenyan women could not afford screening without some form of a subsidy, and 3) self-sampling is perceived to be much more socially acceptable than the current Pap screening process. The Chaguo Letu study went beyond the traditional strategy development process of determining "what" needs to do done by describing in detail "how" the Program should be implemented to be relevant and accessible to all Kenyan women at risk of cervical cancer. This work could potentially facilitate communities of practice and knowledge sharing when addressing other types of health decisions in other low resource settings beyond Kenya.
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 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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".