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Record W4402588887 · doi:10.1177/07334648241263500

How High-Performing Personal Support Workers Set and Maintain Boundaries When Providing Care: A Case Study in Ontario, Canada

2024· article· en· W4402588887 on OpenAlexafffundabout
Elizabeth Kalles, Emily C. King, Paul Holyoke

Bibliographic record

VenueJournal of Applied Gerontology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health ResearchMitacsWomen's College Hospital
KeywordsAgency (philosophy)Boundary (topology)Work (physics)NegotiationSet (abstract data type)NursingPersonal careProfessional boundariesPublic relationsPsychologyMedicineSociologyComputer scienceEngineeringFamily medicinePolitical science

Abstract

fetched live from OpenAlex

Personal support workers (PSWs) provide a large proportion of in-home care services for Canadians. PSWs must negotiate with clients and their family on how prescribed care is delivered. How PSWs set and maintain professional and personal boundaries during care is poorly understood, and failure to manage boundaries can expose both PSWs and clients to risk. High-performing PSWs ( n = 9) and supervisors ( n = 4) within an Ontario, Canada, home care agency were engaged in workshops ( n = 3) to identify field-tested strategies and tactics for identifying, managing, and supporting PSW boundaries. A boundary-management framework was generated, including types of boundary challenges; decision-making principles (e.g., consider the purpose of home care); response strategies (e.g., work with the client on an alternative solution); and tactics for action (e.g., use proactive reminders). Supervisory and organizational supports (e.g., enabling “shop-talk”) were identified. The framework can inform teaching and practice materials for PSWs in Canada and other countries.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.038
GPT teacher head0.331
Teacher spread0.292 · 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 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

Citations1
Published2024
Admission routes3
Has abstractyes

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