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Record W6888564221 · doi:10.20381/ruor-29083

From design to action: participatory approach to capacity building needs for local overdose response plans

2023· other· en· W6888564221 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Ottawa - Library · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCapacity buildingFocus groupOpioid overdoseCitizen journalismParticipatory action researchParticipatory designNeeds assessmentWork (physics)Community-based participatory research

Abstract

fetched live from OpenAlex

Abstract Background In response to the rise in opioid-related deaths, communities across Ontario have developed opioid or overdose response plans to address issues at the local level. Public Health Ontario (PHO) leads the Community Opioid / Overdose Capacity Building (COM-CAP) project, which aims to reduce overdose-related harms at the community level by working with communities to identify, develop, and evaluate capacity building supports for local needs around overdose planning. The ‘From Design to Action’ co-design workshop used a participatory design approach to engage communities in identifying the requirements for capacity building support. Methods A participatory approach (co-design) provided opportunity for collaborative discussion around capacity building needs at the community level. The co-design workshop included three structured collaborative activities to 1) prioritize scenarios that illustrated various challenges associated with community overdose response planning, 2) prioritize the challenges within each scenario and 3) prioritize the supports to address each of these challenges. It was conducted with fifty-two participants involved in opioid/overdose-related response plans in Ontario. Participatory materials were informed by the results of a situational assessment (SA) data gathering process, including survey, interview, and focus group data. A voting system, including dot stickers and discussion notes, was applied to identify priority supports and delivery mechanisms. Results At the workshop, key challenges and top-priority supports were identified, for development and implementation. The prioritized challenges were organized into five categories of capacity building supports addressing: 1) stigma & equity; 2) trust-based relationships, consensus building & on-going communication; 3) knowledge development & on-going access to information and data; 4) tailored strategies and plan adaptation to changing structures and local context; and 5) structural enablers and responsive governance. Conclusion Using a participatory approach, the workshop provided an opportunity for sharing, generating, and mobilizing knowledge to address research-practice gaps at the community level for opioid response planning. The application of health design methods such as the ‘From Design to Action’ co-design workshop supports teams to gain a deeper understanding of needs for capacity building as well as illustrating the application of participatory approaches in identifying capacity building needs for complex public health issues such as the overdose crisis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.097
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.514

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0970.045
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0210.027
Scholarly communication0.0130.009
Open science0.0070.023
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.098
GPT teacher head0.257
Teacher spread0.158 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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