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Record W4414563843 · doi:10.1177/07334648251381410

Who Meets the Emotional Support Needs of Home Healthcare Workers? Characterizing Help-Seeking Practices and Preferences in the Workplace

2025· article· en· W4414563843 on OpenAlexaff
Nicole A. Moreira, Sonia Nizzer, Sandra McKay, D. Linn Holness, Emily C. King

Bibliographic record

VenueJournal of Applied Gerontology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsPublic Health OntarioCanada Research ChairsSt. Michael's HospitalCARE CanadaToronto Metropolitan UniversityUniversity of TorontoOccupational Cancer Research Centre
Fundersnot available
KeywordsEmotional supportLeverage (statistics)Health careSocial supportWork (physics)Perceived organizational support

Abstract

fetched live from OpenAlex

Home healthcare workers (HHCWs) experience high levels of occupational stress, yet their help-seeking practices and preferences for emotional support in the workplace are not well understood. This study was conducted in November and December 2021 at one large home care organization, using a cross-sectional web-based survey to report HHCWs’ ( n = 249; 118 personal support workers, 44 nurses, and 87 rehabilitation providers) current and preferred sources of emotional support at work and perceived barriers. All groups relied heavily on supervisors and expressed comfort sharing with their peers. Respondents reported reluctance to burden others with emotional sharing, not having enough time to seek or use resources, and a desire for more connection with leaders and peers. To meet the emotional support needs of this workforce, organizations can enhance training offered to key organizational leaders and leverage existing members of their workplace support network to provide stronger organization-based support, normalize help-seeking, and sustain a healthy workforce.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.085
GPT teacher head0.426
Teacher spread0.341 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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