Public servants perceptions of the use of scientific evidence in decisions relating to social programmes
DOI:
https://doi.org/10.71068/ss000061Keywords:
implementation, qualitative research, participation, organizational change, sustainabilityAbstract
Introduction: This study examines percepciones de servidores públicos sobre uso de evidencia científica en decisiones relacionadas con programas sociales, focusing on how organizational conditions, resources, participation and governance shape implementation and perceived outcomes. Objective: To analyze quantitative patterns and explain them through participants’ experiences, identifying barriers, facilitators and priorities for improvement. Method: A sequential explanatory mixed-methods design was conducted from October 2024 to January 2025. The quantitative phase included 240 servidores públicos; the qualitative phase comprised 19 semi-structured interviews selected intentionally from participants with direct experience of the phenomenon. All participants provided informed consent. Survey data were summarized using descriptive statistics and internal-consistency indicators, while interviews were transcribed, anonymized and examined through reflexive thematic analysis. Integration connected quantitative tendencies with convergent, divergent and contextual qualitative accounts. Results: The findings showed generally moderate-to-favorable assessments, but implementation varied according to access to resources, practical capabilities, clarity of responsibilities, communication and opportunities for participation. Interview accounts clarified why similar organizational arrangements could be experienced as supportive in some settings and burdensome in others. Conclusions: Effective implementation depends on combining technical arrangements with institutional learning, transparent governance, contextual adaptation and sustained feedback. The mixed-methods integration supports practical decisions without interpreting descriptive associations as causal effects and highlights the need to monitor both implementation conditions and stakeholder experience over time.
Downloads
References
1. Lengnick-Hall R, Proctor EK, Bunger AC, Gerke DR, Martin JK. Ten years of implementation outcome research: a scoping review. Implement Sci. 2022;17:31. Available from: https://doi.org/10.1186/s13012-022-01205-w
2. Bullock HL, Lavis JN, Wilson MG, Mulvale G, Miatello A. Understanding implementation of evidence-informed policies and practices. Implement Sci. 2021;16:18. Available from: https://doi.org/10.1186/s13012-021-01082-7
3. Kuchenmüller T, Boeira L, Oliver S, Moat K, El-Jardali F, Barreto J, et al. Domains and processes for institutionalizing evidence-informed policy-making. Health Res Policy Syst. 2022;20:27. Available from: https://doi.org/10.1186/s12961-022-00820-7
4. Haynes A, Rychetnik L, Finegood D, Irving M, Freebairn L, Hawe P. Applying systems thinking to knowledge mobilisation in public health. Health Res Policy Syst. 2020;18:134. Available from: https://doi.org/10.1186/s12961-020-00600-1
5. MacKillop E. Making sense of knowledge-brokering organisations. Sci Public Policy. 2023;50(6):950-60. Available from: https://doi.org/10.1093/scipol/scad029
6. Yoshizawa L. The imposition of instrumental research use. Am Educ Res J. 2022;59(6):1127-63. Available from: https://doi.org/10.3102/00028312221113556
7. Farley-Ripple EN. The use of research in schools. Educ Sci. 2024;14(6):561. Available from: https://doi.org/10.3390/educsci14060561
8. Ryan A, Prieto-Rodriguez E, Miller A, Gore J. What can implementation science tell us about scaling interventions in school settings? Educ Res Rev. 2024;44:100620. Available from: https://doi.org/10.1016/j.edurev.2024.100620
9. Yu L, Li Y. Artificial intelligence decision-making transparency and employees’ trust. Behav Sci. 2022;12(5):127. Available from: https://doi.org/10.3390/bs12050127
10. Höddinghaus M, Sondern D, Hertel G. The automation of leadership functions: would people trust decision algorithms? Comput Human Behav. 2021;116:106635. Available from: https://doi.org/10.1016/j.chb.2020.106635
11. Shin D. The effects of explainability and causability on perception, trust, and acceptance. Int J Hum Comput Stud. 2021;146:102551. Available from: https://doi.org/10.1016/j.ijhcs.2020.102551
12. Guldmann E, Huulgaard RD. Barriers to circular business model innovation. J Clean Prod. 2020;243:118160. Available from: https://doi.org/10.1016/j.jclepro.2019.118160
13. Centobelli P, Cerchione R, Chiaroni D, Del Vecchio P, Urbinati A. Designing business models in circular economy. Bus Strategy Environ. 2020;29(4):1734-49. Available from: https://doi.org/10.1002/bse.2466
14. Ferasso M, Beliaeva T, Kraus S, Clauss T, Ribeiro-Soriano D. Circular economy business models. Bus Strategy Environ. 2020;29(8):3006-24. Available from: https://doi.org/10.1002/bse.2554
15. Geissdoerfer M, Pieroni MPP, Pigosso DCA, Soufani K. Circular business models: a review. J Clean Prod. 2020;277:123741. Available from: https://doi.org/10.1016/j.jclepro.2020.123741
16. Mishra R, Singh RK, Govindan K. Barriers to adoption of circular economy practices in SMEs. J Clean Prod. 2022;351:131389. Available from: https://doi.org/10.1016/j.jclepro.2022.131389
17. Vidovic D, Reinhardt GY, Hammerton C. Can social prescribing foster individual and community well-being? Int J Environ Res Public Health. 2021;18(10):5276. Available from: https://doi.org/10.3390/ijerph18105276
18. Determinants of digital technology adoption in innovative SMEs. J Innov Knowl. 2024;9(4):100610. Available from: https://doi.org/10.1016/j.jik.2024.100610
19. Barriers to technological innovations of SMEs: how to solve them? Int J Innov Sci. 2020;12(5):545-64. Available from: https://doi.org/10.1108/IJIS-04-2020-0049
20. Braun V, Clarke V. One size fits all? What counts as quality practice in reflexive thematic analysis? Qual Res Psychol. 2021;18(3):328-52. Available from: https://doi.org/10.1080/14780887.2020.1769238
21. Byrne D. A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Qual Quant. 2022;56:1391-412. Available from: https://doi.org/10.1007/s11135-021-01182-y
22. Damschroder LJ, Reardon CM, Widerquist MAO, Lowery J. The updated Consolidated Framework for Implementation Research based on user feedback. Implement Sci. 2022;17:75. Available from: https://doi.org/10.1186/s13012-022-01245-0
23. Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM, et al. A new framework for developing and evaluating complex interventions. BMJ. 2021;374:n2061. Available from: https://doi.org/10.1136/bmj.n2061
24. Chan CKY, Hu W. Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. Int J Educ Technol High Educ. 2023;20:43. Available from: https://doi.org/10.1186/s41239-023-00411-8
25. Chiu TKF. The impact of generative AI on higher education learning and teaching: a study of educators’ perspectives. Comput Educ Artif Intell. 2024;6:100221. Available from: https://doi.org/10.1016/j.caeai.2024.100221
26. Wang K, Ruan Q, Zhang X, Fu C, Duan B. Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Comput Educ Artif Intell. 2024;7:100328. Available from: https://doi.org/10.1016/j.caeai.2024.100328
27. Kamalov F, Santandreu Calonge D, Gurrib I. New era of artificial intelligence in education: towards a sustainable multifaceted revolution. Sustainability. 2023;15:12451. Available from: https://doi.org/10.3390/su151612451
28. Braganza A, Chen W, Canhoto A, Sap S. Productive employment and decent work: the impact of AI adoption on psychological contracts, job engagement and employee trust. J Bus Res. 2021;131:485-94. Available from: https://doi.org/10.1016/j.jbusres.2020.08.018
29. Leicht-Deobald U, Busch T, Schank C, Weibel A, Schafheitle S, Wildhaber I, et al. The challenges of algorithm-based HR decision-making for personal integrity. J Bus Ethics. 2022;160:377-92. Available from: https://doi.org/10.1007/s10551-019-04204-w
30. Jacovi A, Marasović A, Miller T, Goldberg Y. Formalizing trust in artificial intelligence: prerequisites, causes and goals of human trust in AI. Proc ACM Hum Comput Interact. 2021;5(CSCW2):1-26. Available from: https://doi.org/10.1145/3479523
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Segundo Alexander Rosado Martínez
This work is licensed under a Creative Commons Attribution 4.0 International License.
Los artículos publicados en esta revista están disponibles bajo la licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0). Esta licencia autoriza a terceros a descargar, copiar, distribuir, adaptar y reutilizar los contenidos, incluso con fines comerciales, siempre que se otorgue el crédito correspondiente al autor original de la obra.
