Building Relationships, Forming Collaborations: Lessons Learned From an Unconference Seeking to Cultivate Solutions in Healthcare
Bibliographic record
Abstract
INTRODUCTION: Calls for a 'major rethinking' of the delivery of healthcare services are echoed across Canada as the healthcare crisis continues. Proposed strategies to address the challenges of this crisis include: a transdisciplinary approach that is patient-focused and community-based; a representative team composed of patients, caregivers, healthcare providers, decision makers and policymakers; and authentic collaboration among stakeholder groups throughout the research cycle. OBJECTIVE: This study aimed to enable community members to take on a leading role in building capacity and to provide a space for discourse among diverse groups while respecting community wisdom, values and priorities. METHODS: The Collaborative Health Research Institute of Southern Alberta (CHRISA) organized a participant-oriented Unconference event to address the factors contributing to the healthcare crisis in Alberta, Canada. An Unconference is a participant-oriented meeting where the attendees nominate the topics, agree on the agenda and lead the sessions. This article describes the Unconference programme and presents the findings from a thematic analysis of the discussion notes from breakout sessions, feedback from participants (i.e., lessons learned) and pragmatic recommendations for future Unconference events. RESULTS: Findings from sessions included the following: (1) identifying the 'wicked' problems, (2) the factors/causes contributing to each problem (i.e., contributors) and (3) potential multifaceted solutions or ideas to remedy the problem. Lessons learned from the postevent evaluation resulted in six recommendations for organizing future Unconferences. CONCLUSION: The CHRISA Unconference achieved its goals by providing a venue for attendees to connect, engage and network on topics of interest, explore new ways of addressing challenges in healthcare and serve as a foundation for future initiatives and collaborations in healthcare research and practice. PATIENT OR PUBLIC CONTRIBUTION: The Unconference was attended by community members who identify as patients, frontline workers, programme administrators and representatives of public organizations and agencies. Participants contributed to breakout session discussions, provided feedback on the Unconference and offered recommendations for future events. The co-authors are service users, people with lived experience or those work in the healthcare setting; they have been involved in data collection, analysis and interpretation, and contributed to this report.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".