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Record W4402602381 · doi:10.1186/s43058-024-00636-2

Views and perspectives toward implementing the Global Spine Care Initiative (GSCI) model of care, and related spine care program by the people in Cross Lake, Northern Manitoba, Canada: a qualitative study using the Theoretical Domain Framework (TDF)

2024· article· en· W4402602381 on OpenAlexafffundabout
Nicole Robak, Elena Broeckelmann, Silvano Mior, Melissa Atkinson-Graham, Jennifer Ward, Muriel Scott, Steven Passmore, Deborah Kopansky-Giles, Patrícia Tavares, Jean Moss, Jacqueline Ladwig, Cheryl M. Glazebrook, David A. Monias, Helga Hamilton, Donnie McKay, Randall Smolinski, Scott Haldeman, André Bussières

Bibliographic record

VenueImplementation Science Communications · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcGill University Health CentreCanadian Memorial Chiropractic CollegeCanadian Red Cross SocietyOntario Tech UniversityCARE CanadaUniversity of TorontoUniversité du Québec à Trois-RivièresUniversity of Manitoba
FundersHealth CanadaSkoll Foundation
KeywordsSPINE (molecular biology)Qualitative researchMedicineNursingSociologySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Back pain is very common and a leading cause of disability worldwide. Due to health care system inequalities, Indigenous communities have a disproportionately higher prevalence of injury and acute and chronic diseases compared to the general Canadian population. Indigenous communities, particularly in northern Canada, have limited access to evidence-based spine care. Strategies established in collaboration with Indigenous peoples are needed to address unmet healthcare needs, including spine care (chiropractic and movement program) services. This study aimed to understand the views and perspectives of Cross Lake community leaders and clinicians working at Cross Lake Nursing Station (CLNS) in northern Manitoba regarding the implementation of the Global Spine Care Initiative (GSCI) model of spine care (MoC) and related implementation strategies. METHOD: A qualitative exploratory design using an interpretivist paradigm was used. Twenty community partners were invited to participate in semi-structured interviews underpinned by the Theoretical Domains Framework (TDF) adapted to capture pertinent information. Data were analyzed deductively and inductively, and the interpretation of findings were explored in consultation with community members and partners. RESULTS: Community leaders (n = 9) and physicians, nurses, and allied health workers (n = 11) emphasized: 1) the importance of contextualizing the MoC (triaging and care pathway) and proposed new services through in-person community engagement; 2) the need and desire for local non-pharmacological spine care approaches; and 3) streamlining patient triage and CLNS workflow. Recommendations for the streamlining included reducing managerial/administrative duties, educating new incoming clinicians, incorporating follow-up appointments for spine pain patients, and establishing an electronic medical record system along with a patient portal. Suggestions regarding how to sustain the new spine care services included providing transportation, protecting allocated clinic space, resolving insurance coverage discrepancies, addressing misconceptions about chiropractic care, instilling the value of physical activity for self-care and pain relief, and a short-term (30-day) incentivised movement program which considers a variety of movement options and offers a social component after each session. CONCLUSION: Community partners were favorable to the inclusion of a refined GSCI MoC. Adapting the TDF to unique Indigenous needs may help understand how best to implement the MoC in communities with similar needs.

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)0.009
metaresearch head score (Gemma)0.010
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: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0200.013
Scholarly communication0.0050.002
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.083
GPT teacher head0.486
Teacher spread0.403 · 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".

Quick stats

Citations2
Published2024
Admission routes3
Has abstractyes

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