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Record W4388523444 · doi:10.3389/fpubh.2023.1166106

Impact of the COVID-19 pandemic on community-based brain injury associations across Canada: a cross-sectional survey study

2023· article· en· W4388523444 on OpenAlexafffundabout
Ana Paula Salazar, Carolina Bottari, Sophie Lecours, Michelle M. McDonald, Monique A. M. Gignac, Bonnie Swaine, Julia Schmidt, Carolyn Lemsky, Ashley Brosda, Lisa Engel

Bibliographic record

VenueFrontiers in Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of ManitobaInstitute for Work & HealthUniversity of British ColumbiaPublic Health OntarioCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversity of TorontoUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersCanadian Institutes of Health Research
KeywordsPsychosocialLonelinessPandemicPublic healthCross-sectional studyMedicineDescriptive statisticsFamily medicineAnxietyPsychologyNursingPsychiatryCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

Background: The COVID-19 pandemic created new difficulties for people living with brain injury, their families, and caregivers while amplifying the challenges of community-based associations that support them. We aimed to understand the effects of the pandemic on clients who live with brain injury, as well as on the provision of community brain injury services/programs in Canada. Methods: Online cross-sectional survey conducted in January 2022. Representatives of brain injury associations across Canada completed the 31 open- and closed-ended questions about meeting clients' needs, addressing public health guidelines, and sustaining the association. Data were analyzed using descriptive statistics (close-ended questions) and qualitative content analysis (open-ended questions). Results: Of the 45 key representatives from associations in Pacific/Western (40%), Central (56%), and Atlantic Canada (4%), the majority were paid executive directors (67%). Participants reported that the most frequent psychosocial challenges experienced by their clients during the pandemic were social isolation (98%), loneliness (96%), and anxiety (93%). To alleviate these challenges, associations implemented wellness checks and psychosocial support. Most respondents (91%) affirmed that clients faced multiple technological barriers, such as a lack of technological knowledge and financial resources for devices and/or internet. In the open-ended questions, twenty-nine (64%) associations reported providing clients with devices, technology training, and assistance. Regarding public health measures, thirty (67%) respondents reported that clients had challenges understanding and/or following public health guidelines. Forty-two associations (93%) provided tailored information to help clients understand and comply with public health measures. Although associations (67%) received pandemic-related funding from the Canadian government they still struggled with the association's sustainability. Thirty-four (76%) lost funding or financial resources that prevented them from delivering programs or required the use of reserve funds to continue to do so. Only 56% reported receiving sufficient funding to address additional COVID-19-related expenses. Conclusion: Although the pandemic added further challenges to the sustainability of brain injury associations across Canada, they quickly adapted services/programs to respond to the increasing and varied needs of clients, while complying with protective measures. To ensure community associations' survival it is essential to aptly recognize the vital role played by these associations within the brain injury care continuum.

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)0.002
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.366
GPT teacher head0.514
Teacher spread0.148 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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