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Record W4281611207 · doi:10.1002/cfp2.1145

Work‐life balance and burnout in financial planning profession

2022· article· en· W4281611207 on OpenAlexaff
Aman Sunder, Jennifer Lehman, Rebecca Henderson

Bibliographic record

VenueFinancial Planning Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsCentennial College
Fundersnot available
KeywordsDepersonalizationWork–life balanceBurnoutFlexibility (engineering)CertificationJob satisfactionLife satisfactionBalance (ability)PsychologyWork (physics)DemographicsPersonal lifeEmotional exhaustionBusinessSocial psychologyNursingApplied psychologyFinanceClinical psychologyMedicineManagementPolitical scienceSociologyEconomicsDemography

Abstract

fetched live from OpenAlex

Abstract This study examines the work environment outcomes in the financial planning profession, job‐related burnout, and work‐life balance satisfaction, using a convenience sample of survey respondents employed in the financial planning profession. The study compares subgroups based on gender, job role, firm size, and their CFP® certification controlling for relevant factors such as type of industry, weekly working hours, work‐life conflict, the flexibility provided by the employer, and demographics. Results indicated that the women in smaller firms are more satisfied with work‐life balance. In general, smaller firm sizes are associated with lower personal accomplishment, higher satisfaction from work‐life balance, and lower impersonal response toward clients measured by Depersonalization scores of Maslach Burnout Inventory (MBI). MBI Depersonalization scores are higher for CEOs and owners of the firms. In addition, the CFP® certification is associated with greater satisfaction from work‐life balance. The results build on previous research to provide insight into aspects that can improve participation in the profession. Future research should find ways to attract diverse participants to improve success and satisfaction outcomes for all professionals and firm sizes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.501
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.334
Teacher spread0.295 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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