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Record W3037114976 · doi:10.1177/1038416220919827

Improving career wellbeing for first-time expectant mothers

2020· article· en· W3037114976 on OpenAlexafffund
Charles P. Chen, Lindsay Morris

Bibliographic record

VenueAustralian Journal of Career Development · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNegotiationCareer developmentPsychologyIdentity (music)PopulationFace (sociological concept)NarrativeCareer counselingVocational educationPersonal developmentPublic relationsSocial psychologySociologyPedagogyPolitical scienceSocial sciencePsychotherapist

Abstract

fetched live from OpenAlex

Within the diverse population of working women, those who experience pregnancy for the first time may face some particular challenges when it comes to their career development needs and issues. These include discrimination in the workplace, responding to social expectations and pressures, negotiating life roles, and evolving personal identities. This article discusses the major career problems encountered by this target group, both structurally and socially, with a focus on individual strategies to access personal agentic functioning and empower women facing these challenges. These workers are often overlooked in the career literature; yet, there is a range of career theories to draw upon to assist them in their needs. The application of the life-span, life-space career theory, and the narrative therapy approaches are explored in relation to the helping process. These two theoretical orientations were chosen as they address the particular challenges faced by pregnant women in the workplace, especially around negotiating life roles and an evolving personal identity. There is a need for a stronger understanding of these challenges and opportunities to support pregnant women as they seek vocational wellbeing, and how to tailor suitable, well-established career counselling strategies to meet their unique needs.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.058
GPT teacher head0.277
Teacher spread0.219 · 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 designNot applicable
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

Citations4
Published2020
Admission routes2
Has abstractyes

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