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Record W2918801614 · doi:10.22215/etd/2019-13488

Looking Beneath the Surface: The Personal and Family Consequences of Occupational Injuries

2019· dissertation· en· W2918801614 on OpenAlexafffund
Ana Dursun

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsCarleton University
FundersMinistère de la Défense Nationale
KeywordsSpousePsychologyContext (archaeology)DistressSocial psychologyPsychological distressEmotional distressClinical psychologyMental healthPsychiatryAnxietyPolitical science

Abstract

fetched live from OpenAlex

To date, the individual and familial consequences that may ensue following a workplace injury have not been fully examined.In this research, I examine how mental and physical workplace injuries influence personal and family outcomes, within a military organizational context.The results of Study 1 (N = 888) indicate that occupational injuries are related to psychological well-being, relationship with spouse, and relationship with children.Study 2 (N =1836) examines the relationship between injuries of military members and the well-being of military spouses.The results indicate that having an injured military member spouse is related to psychological distress, relationship satisfaction and emotional intimate partner violence.Furthermore, it appears that some of these effects are buffered when spouses have financial stability, a higher sense of organizational support, and satisfaction with the organization.This research contributes to the broad understanding of workplace injuries, and has implications for future research and practice.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.482
Teacher spread0.389 · 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 designQualitative
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
Published2019
Admission routes2
Has abstractyes

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