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Record W4406781951 · doi:10.1177/14624745251315270

Who cares?: The burdens of care borne by the loved ones of incarcerated men

2025· article· en· W4406781951 on OpenAlexaffabout
Alysha McDonald, Luca Berardi, Kevin D. Haggerty, Sandra M. Bucerius

Bibliographic record

VenuePunishment & Society · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of AlbertaMcMaster University
Fundersnot available
KeywordsPrisonContext (archaeology)Value (mathematics)PsychologyHealth careNursingExtension (predicate logic)CriminologySociologyMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

The labor and associated challenges of providing informal care have been characterized as constituting a burden of care (BoC). Despite the analytical value this concept has demonstrated in studies of nursing, psychiatry, and health administration, researchers have overlooked this phenomenon in the context of incarceration. Using 181 longitudinal interviews conducted between April 2020 and January 2021 with 29 loved ones of men incarcerated across Canada, we examine the experiences of caregivers trying to meet the outstanding needs of incarcerated people. We find that the BoC arose when caregivers compensated for the correctional system's (real or perceived) failed responsibility to fulfill the basic needs of incarcerated people relating to rehabilitation and release planning, as well as their legal advocacy requirements. We position the BoC as a component of secondary prisonization and use the framework to accentuate the roles caregivers played in reducing the care deficit incarcerated people experienced and, by extension, augmenting the functionality of the prison system. We suggest ways to promote less onerous carceral and reintegrative terms by reducing the unmet needs of incarcerated people and, by extension, the BoC that caregivers bore.

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 score0.109
Threshold uncertainty score0.829

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 studies0.0010.000
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 head0.018
GPT teacher head0.370
Teacher spread0.351 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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