The Pentahelix Partnership Responses during Covid-19 Pandemic in Makassar
Bibliographic record
Abstract
Background: A Partnership is one of the strategies for accelerating responses of Covid-19, especially in understanding the decisions made by the government and the various reactions of the community regarding the Covid-19 pandemic. Objective: This study aims to explore partnership efforts pentahelix involving the government, academia, community, business sectors, and the media in handling Covid-19. Methods: The qualitative study was carried out through in-depth interviews with 18 participants consisting of government, media, academics, and community elements. The participants were members of the Covid-19 Handling Task Force (Covid-19 Task Force) and people who were directly involved in handling Covid-19 in Makassar. The content analysis was performed using the collected data. The themes that emerge from the data are the pentahelix partnership with the government, community groups, academics, business sectors, and mass media. The data were collected from December 2022 to March 2023. Results: The pentahelix partnership involves 5 parties: government, community, academics, business sectors, and the media. The partnership is accomplished through coordination, collaboration, participation, and mutual assistance. This partnership is established in the implementation of government policies in handling Covid-19 with the formation of Task Forces where the community participates in its implementation. Coordination between the government and academics regarding ways to resolve the Covid-19 case; collaboration with business sectors in providing facilities and infrastructure for handling Covid-19 activities as well as with the media that assist in disseminating information, public education, and news related to the incident of Covid-19 was adopted to implement the health policies during this pandemic. Conclusion: handling covid-19 with pentahelix partnerships was carried out through coordination, collaboration, participation, and mutual assistance of all parties. The partnership process is a strategy for implementing health programs related to the handling of Covid-19 which are regulated by the government.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".