What Drives Knowledge Seeking, Sharing, and Use Among Family Planning Professionals? Behavioral Evidence From Africa, Asia, and the United States
Bibliographic record
Abstract
OBJECTIVE: To contribute to strengthening family planning and reproductive health (FP/RH) programs by identifying behavioral factors that influence FP/RH professionals' knowledge management (KM) behaviors. METHODS: We conducted an online survey, in-depth interviews, and cocreation workshops between July 2019 and June 2020 with a convenience sample of FP/RH professionals in Africa, Asia, and the United States to explore their KM behaviors. We used descriptive statistics to analyze the survey data and inductive thematic analysis for the interviews, and we synthesized participant inputs from selected cocreation activities. RESULTS: The samples consisted of 273 survey respondents, 23 interviewees, and 69 cocreation workshop participants. There were no significant differences in how professionals seek and share information by gender, role, or geographic region, except related to language barriers among Francophone professionals. FP/RH professionals reported using both digital sources and their professional networks to seek and share information. Choice overload and cognitive overload (when people are presented with too much information and in a way that is hard to understand, respectively) act as barriers as they seek and use information. Too many information sources lead to frustration and inaction and best practices are often not contextualized or specific enough for application. Positive KM organizational cultures help facilitate effective information sharing, but reluctance to share information persists due to fear of losing comparative advantage. FP/RH professionals noted that such barriers result in duplication of effort and lack of advancement in FP/RH programs. CONCLUSION: To improve overall program impact, KM interventions in FP/RH and global health should reduce cognitive and choice overload, especially by curating and sharing practical, actionable information with essential details on context and how programs are implemented so that others can apply or adapt the learnings. Programs should use incentives to foster motivation to share this type of information.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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, 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".