A qualitative case study of evaluation use in the context of a collaborative program evaluation strategy in Burkina Faso
Bibliographic record
Abstract
BACKGROUND: Program evaluation is widely recognized in the international humanitarian sector as a means to make interventions and policies more evidence based, equitable, and accountable. Yet, little is known about the way humanitarian non-governmental organizations (NGOs) actually use evaluations. METHODS: The current qualitative evaluation employed an instrumental case study design to examine evaluation use (EU) by a humanitarian NGO based in Burkina Faso. This organization developed an evaluation strategy in 2008 to document the implementation and effects of its maternal and child healthcare user fee exemption program. Program evaluations have been undertaken ever since, and the present study examined the discourses of evaluation partners in 2009 (n = 15) and 2011 (n = 17). Semi-structured individual interviews and one group interview were conducted to identify instances of EU over time. Alkin and Taut's (Stud Educ Eval 29:1-12, 2003) conceptualization of EU was used as the basis for thematic qualitative analyses of the different forms of EU identified by stakeholders of the exemption program in the two data collection periods. RESULTS: Results demonstrated that stakeholders began to understand and value the utility of program evaluations once they were exposed to evaluation findings and then progressively used evaluations over time. EU was manifested in a variety of ways, including instrumental and conceptual use of evaluation processes and findings, as well as the persuasive use of findings. Such EU supported planning, decision-making, program practices, evaluation capacity, and advocacy. CONCLUSIONS: The study sheds light on the many ways evaluations can be used by different actors in the humanitarian sector. Conceptualizations of EU are also critically discussed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.182 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".