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Record W7126243506 · doi:10.1017/flw.2026.10008

Increasing financial confidence in Canadian women

2025· article· en· W7126243506 on OpenAlexaffabout
Johanna Peetz, Andrea L. Howard, Samantha J. Hollingshead, Monica Soliman

Bibliographic record

VenueJournal of Financial Literacy and Wellbeing · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsPublic Health Agency of CanadaCarleton University
Fundersnot available
KeywordsCompetence (human resources)Financial managementPsychological interventionIntervention (counseling)Financial literacyGender gap

Abstract

fetched live from OpenAlex

Abstract Women tend to be underconfident about their financial knowledge. In this longitudinal study, we tested two interventions intended to raise financial confidence and engagement in positive financial management behaviors among young Canadian women ( N = 1119). One intervention included a brief educational task, teaching participants definitions of financial terms. Another intervention challenged social beliefs about financial competence by prompting participants to describe and browse other women’s stories about financial competence experiences on a website. Directly after the interventions, financial confidence ratings from women assigned to either or both of the intervention conditions were about 6% higher than ratings in a control condition. This effect persisted one week later, though a month later the size of the effect had dropped to non-significance. Confidence was linked to better financial management behaviors and more savings. Results also showed that participants in all conditions reported higher financial confidence and better financial management behaviors at later vs. earlier surveys. We conclude that simply reporting on financial attitudes and behaviors over time can increase women’s financial confidence and recommend fostering discourse about finances to close the gender gap in financial confidence.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.148
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.004
GPT teacher head0.222
Teacher spread0.218 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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