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Record W4250987833 · doi:10.1177/082585971002600203

Family Caregivers of Palliative Cancer Patients at Home: The puzzle of Pain Management

2010· article· en· W4250987833 on OpenAlexafffund
Anita Mehta, S. Robin Cohen, Franco A. Carnevale, Hélène Ezer, Francine M. Ducharme

Bibliographic record

VenueJournal of Palliative Care · 2010
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de MontréalMcGill UniversityJewish General HospitalMontreal General Hospital
FundersCanadian Institutes of Health Research
KeywordsGrounded theoryTheoretical samplingPain managementPalliative careCoding (social sciences)Family caregiversPsychologyAxial codingNursingHealth careCancer painHealth professionalsQualitative researchMedicineAlternative medicineSociologyPhysical therapy

Abstract

fetched live from OpenAlex

The purpose of this grounded theory study was to understand the processes used by family care-givers to manage the pain of cancer patients at home. A total of 24 family caregivers participated. They were recruited using purposeful then theoretical sampling. The data sources were taped, transcribed (semi-structured) interviews and field notes. Data analysis was based on Strauss and Corbin's (1998) requirements for open, axial, and selective coding. The result was an explanatory model titled “the puzzle of pain management,” which includes four main processes: “drawing on past experiences”; “strategizing a game plan”; “striving to respond to pain”; and “gauging the best fit,” a decision-making process that joins the puzzle pieces. Understanding how family caregivers assemble their puzzle pieces can help health care professionals make decisions related to the care plans they create for pain control and help them to recognize the importance of providing information as part of resolving the puzzle of pain management.

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)0.000
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 score0.066
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.054
GPT teacher head0.372
Teacher spread0.318 · 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

Citations13
Published2010
Admission routes2
Has abstractyes

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