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Record W3204917059 · doi:10.1177/01902725211045024

The Divergent Mental Health Effects of Dashed Expectations and Unfulfilled Aspirations: Evidence from American Lawyers’ Careers

2021· article· en· W3204917059 on OpenAlexaff
Ioana Sendroiu, Laura Upenieks, Markus H. Schafer

Bibliographic record

VenueSocial Psychology Quarterly · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental healthPsychological resiliencePsychologySocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Considerable work has shown that optimistic future orientations can be a resource for resilience across individuals’ lives. At the same time, research has shown little downside to “shooting for the stars” and failing. Here, we bring these competing insights to the study of lawyers’ careers, investigating the relationship between mental health and failure in achieving desired career advancement. To do this, we differentiate between expectations and aspirations for the future, a conceptual distinction that has been much theorized but little tested. Using longitudinal data, we show that dashed expectations of making partner are associated with depreciated mental health outcomes, whereas a similar relationship does not exist for unfulfilled aspirations. We conclude that inasmuch as expectations are more deeply rooted in an individual’s realistic sense of their future self, failing to achieve what is expected is more psychologically damaging than failing to achieve what is simply aspired. Our findings contrast with studies of younger people that demonstrate fewer consequences for unfulfilled future orientations, and so we highlight the importance of specifying how particular future-oriented beliefs fit into distinct career and life course trajectories, for better or for worse. In the process, we contribute to the academic literatures on future orientations, work, and mental health.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.586

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 studies0.0010.001
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 head0.065
GPT teacher head0.425
Teacher spread0.360 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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