MétaCan
Menu
Back to cohort
Record W2892966028 · doi:10.1186/s12939-018-0820-2

Disruption as opportunity: Impacts of an organizational health equity intervention in primary care clinics

2018· article· en· W2892966028 on OpenAlexafffundabout
Annette J. Browne, Colleen Varcoe, Marilyn Ford‐Gilboe, C. Nadine Wathen, Victoria Smye, Beth Jackson, Bruce Wallace, Bernie Pauly, Carol P. Herbert, Josée G. Lavoie, Sabrina T. Wong, Amélie Blanchet Garneau

Bibliographic record

VenueInternational Journal for Equity in Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité de MontréalUniversity of ManitobaManitoba HealthCentre for Family MedicineUniversity of VictoriaPublic Health Agency of CanadaWestern UniversityUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersCanadian Institutes of Health Research
KeywordsHealth equityNursingHealth careMedicinePublic healthSocial determinants of healthEquity (law)Public relationsPsychologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The health care sector has a significant role to play in fostering equity in the context of widening global social and health inequities. The purpose of this paper is to illustrate the process and impacts of implementing an organizational-level health equity intervention aimed at enhancing capacity to provide equity-oriented health care. METHODS: The theoretically-informed and evidence-based intervention known as 'EQUIP' included educational components for staff, and the integration of three key dimensions of equity-oriented care: cultural safety, trauma- and violence-informed care, and tailoring to context. The intervention was implemented at four Canadian primary health care clinics committed to serving marginalized populations including people living in poverty, those facing homelessness, and people living with high levels of trauma, including Indigenous peoples, recent immigrants and refugees. A mixed methods design was used to examine the impacts of the intervention on the clinics' organizational processes and priorities, and on staff. RESULTS: Engagement with the EQUIP intervention prompted increased awareness and confidence related to equity-oriented health care among staff. Importantly, the EQUIP intervention surfaced tensions that mirrored those in the wider community, including those related to racism, the impacts of violence and trauma, and substance use issues. Surfacing these tensions was disruptive but led to focused organizational strategies, for example: working to address structural and interpersonal racism; improving waiting room environments; and changing organizational policies and practices to support harm reduction. The impact of the intervention was enhanced by involving staff from all job categories, developing narratives about the socio-historical context of the communities and populations served, and feeding data back to the clinics about key health issues in the patient population (e.g., levels of depression, trauma symptoms, and chronic pain). However, in line with critiques of complex interventions, EQUIP may not have been maximally disruptive. Organizational characteristics (e.g., funding and leadership) and characteristics of intervention delivery (e.g., timeframe and who delivered the intervention components) shaped the process and impact. CONCLUSIONS: This analysis suggests that organizations should anticipate and plan for various types of disruptions, while maximizing opportunities for ownership of the intervention by those within the organization. Our findings further suggest that equity-oriented interventions be paced for intense delivery over a relatively short time frame, be evaluated, particularly with data that can be made available on an ongoing basis, and explicitly include a harm reduction lens.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0020.001
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.610
GPT teacher head0.737
Teacher spread0.126 · 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 designObservational
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

Citations155
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal for Equity in HealthSame topicHealth Policy Implementation ScienceFrench-language works237,207