Attitudes, barriers, and enablers towards conducting primary care research in Banda Aceh, Indonesia: a qualitative research study
Bibliographic record
Abstract
BACKGROUND: Conducting university-based research is important for informing primary care, especially in lower- and middle- income countries (LMICs) such as Indonesia. Syiah Kuala University (SKU), the largest educational institution in Aceh province, Indonesia, is actively establishing itself as a leader in research innovation; however, this effort has not yet demonstrated optimum results. Understanding faculty members' perceptions of how research is conducted in this setting is crucial for the design and implementation of successful and sustainable research strategies to increase the quantity and quality of primary care research conducted at LMIC universities. The objective of this study was to identify current attitudes, barriers and enablers/facilitators towards primary care research participation and implementation in this higher education institution. METHODS: A descriptive-interpretive qualitative study was conducted. 29 participants, representing 90% of all faculty members providing primary care, were included. A mixed-methods approach was used, combining the use of a participant survey with 10 focus group discussions. Participants were encouraged to complete the survey in either English or Bahasa Indonesia. All of the focus group discussions were recorded, transcribed and translated into English. Thematic content analysis of these transcripts was carried out. RESULTS: The majority of participants agreed that SKU has set research as a priority, as it is one of the three pillars of higher education, mandatory in all Indonesian higher education institutions. This research identified many barriers in conducting research, i.e. weak research policy, lack of research funding and infrastructure, complicated research bureaucracy and administrative process, as well as time constraints for conducting research relative to other duties. Participants expressed that personal motivation was a very important enabler/facilitator for increasing research activities. In order to improve research productivity, the majority of participants suggested that having local awards and formal recognition, having the opportunity to partner with local business and communities, provision of incentives, and having access to a research help-desk would be beneficial. CONCLUSIONS: Generally, participants showed a supportive and positive attitude towards research, and provided examples of how to improve research productivity in the Asian university context.
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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 | 0.043 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".