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Record W2915848909 · doi:10.3138/jmvfh.5.s1.2018-0029

The impact of financial satisfaction on well-being of Canadian military members

2019· article· en· W2915848909 on OpenAlexaffvenueabout
Cynthia Wan, M. Katharine Berlinguette, Alla Skomorovsky

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsDepartment of National Defence
Fundersnot available
KeywordsLife satisfactionFinancial distressCoping (psychology)Psychological distressPsychologyDistressMultilevel modelFinanceDebtClinical psychologySocial psychologyMental healthBusinessPsychiatry

Abstract

fetched live from OpenAlex

Introduction: The ability to attain financial satisfaction is becoming progressively difficult due to increasing living standards, costs, and accruement of debt. Military members are arguably at greater risk of being dissatisfied with their current financial situation and suffering from financial strain and psychological distress due to the demands of a military lifestyle. Methods: The present study aimed to explore the predictive relationships between financial satisfaction and daily coping ability on two facets of well-being – life satisfaction and psychological distress – among Canadian military personnel. Results: The hierarchical regression results demonstrated that financial satisfaction was a significant predictor of life satisfaction and psychological distress. Moreover, daily coping ability played a vital role in improving participants’ life satisfaction and reducing psychological distress, above and beyond the positive impact of financial satisfaction. Discussion: The qualitative difference between the predictors and outcome variables as well as directions for future research are discussed.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.066
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.021
GPT teacher head0.320
Teacher spread0.298 · 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

Citations6
Published2019
Admission routes3
Has abstractyes

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