MétaCan
Menu
Back to cohort
Record W4404502922 · doi:10.1108/edi-12-2023-0457

Engaging workers with disabilities in the financial sector: exploring promising practices through key informant interviews and a rapid literature review

2024· article· en· W4404502922 on OpenAlexaffabout
Alexis Buettgen, Andrea Gardiola, Emile Tompa

Bibliographic record

VenueEquality Diversity and Inclusion An International Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsMcMaster UniversityQueen's UniversityWilfrid Laurier UniversityInstitute for Work & Health
Fundersnot available
KeywordsKey (lock)PsychologySociologyBusiness

Abstract

fetched live from OpenAlex

Purpose This study explores the challenges, barriers and opportunities for engaging persons with disabilities in employment in the Canadian financial sector. Design/methodology/approach We situated this research within a critical disability conceptual framework to add to existing theories of employee engagement. We conducted an exploratory qualitative study of key informant interviews of the experiences of diverse persons with disabilities in the Canadian financial sector. Findings We found that the financial sector has the potential to be a leader in the engagement of workers with disabilities. Key challenges include corporate bureaucracy and a focus on aggressive growth that perpetuates ableist norms of individualism, self-reliance and competitive achievement. Key informant interviews indicated that opportunities for engagement can be fostered by committed leadership, inclusive corporate culture, supportive management, and respectful and responsive workplace accommodations. Originality/value This article contributes to the literature on opportunities for equity, diversity and inclusion at work through a critical exploration of the challenges and promising practices associated with supporting engagement of persons with disabilities in the Canadian financial sector and beyond.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.442
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.004
Open science0.0000.002
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 head0.201
GPT teacher head0.409
Teacher spread0.208 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueEquality Diversity and Inclusion An International JournalSame topicDisability Education and EmploymentFrench-language works237,207