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Record W4360978643 · doi:10.1080/01621424.2023.2193560

Service delivery and programing adaptations for individuals with disabilities by municipalities and non-profit organizations during the COVID pandemic

2023· article· en· W4360978643 on OpenAlexafffund
Nolwenn Lapierre, Dylane Labrie, François Routhier, W. Ben Mortenson

Bibliographic record

VenueHome Health Care Services Quarterly · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsGF Strong Rehabilitation CentreCentre for Interdisciplinary Research in RehabilitationCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleInternational Collaboration On Repair DiscoveriesUniversity of British ColumbiaUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsPandemicFeelingQualitative researchCoronavirus disease 2019 (COVID-19)Service delivery frameworkPublic relationsCoping (psychology)BusinessFlexibility (engineering)Service (business)PsychologyNursingMarketingMedicineSociologyPolitical scienceSocial psychologyManagement

Abstract

fetched live from OpenAlex

Municipalities and nonprofit organizations play a major role in administrating services that support individuals with disabilities. The purpose of this study was to explore how these organizations responded to the COVID-19 pandemic in regards to service delivery and programming for people with disabilities. This qualitative interpretative description study used semi-structured individual interviews for data collection. Recordings of the interviews were transcribed. Then the transcripts were analyzed qualitatively for themes following an inductive approach. Twenty-six individuals working for nonprofit organization or municipalities participated in the study. Six themes were identified: doing more with less; adapting rather than creating new services; ongoing consultation with stakeholders; feeling successful at adapting the services; being innovative with fundraising and embracing radical change. Flexibility and iterative user-centered approach appeared to be common coping strategies. Remote services were privileged to adapt service delivery during the COVID-19 pandemic.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.302
Teacher spread0.286 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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