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Record W4290998687 · doi:10.54656/jces.v14i2.31

When Is a Partnership Not a Partnership? Reflecting on Inherent Challenges in University-Community Collaborations on Educational Programs

2022· article· en· W4290998687 on OpenAlexaffabout
Alyson E. King, Allyson Eamer, Shanti Fernando

Bibliographic record

VenueJournal of Community Engagement and Scholarship · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsGeneral partnershipQualitative researchPublic relationsPower (physics)Process (computing)Medical educationQualitative propertyPsychologySociologyNursingPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

In our research on supported education (SEd) programs in Canadian psychiatric hospitals, community-university partnerships have offered hopeful findings that demonstrate the potential for improved social and educational outcomes for patients in these programs. There were inherent challenges associated with conducting academic research on these programs alongside nonacademic partners. While some of our research collaborators, who were patient-educators with varying backgrounds, were fully engaged in the research process, others were only somewhat engaged, and some wanted minimal involvement. Because most psychiatric hospital–based research involves medical or pharmaceutical research, we did not locate established frameworks that could be used as models for our educational qualitative research. Although we encountered some obstacles to fully engaged partnerships, we still conducted productive collaborations that resulted in rich, broadly useful qualitative and quantitative data from interviews with students, teachers, and administrators. That being said, we found that in trying to respect the limited time availability of our partners, we ended up with less input from our partners than we had originally hoped for. The lessons we learned—such as the need for clearer role definitions and strategies to manage power imbalances, conflicting objectives, and time constraints faced by nonacademic collaborators—may be applied to other projects that engage community partners whose time and capacity constraints may inhibit their full engagement, such as municipalities supporting long-term care homes or emergency shelters.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.009
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.798
GPT teacher head0.510
Teacher spread0.288 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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