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Seniors’ decision making about pain management

2001· article· en· W2159991859 on OpenAlexaffabout
Margaret M. Ross, Anne Carswell, Malcolm Hing, Gary R. Hollingworth, William Dalziel

Bibliographic record

VenueJournal of Advanced Nursing · 2001
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsOttawa HospitalUniversity of British ColumbiaUniversity of OttawaMinistry of Health and Long Term Care
FundersWorld Health Organization
KeywordsContext (archaeology)Health careExploratory researchFocus groupControl (management)MedicinePain managementPsychologyNursingPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Musculoskeletal pain is a problem with which many seniors must contend, many on a daily basis. Little is known, however, of the self-care decisions that seniors make regarding the management and control of this pain. These decisions can influence in a significant manner the delivery of health care to seniors and their overall health and well-being. Purpose. The purpose of our exploratory-descriptive study was to investigate seniors' decision making regarding the management and control of musculoskeletal pain by gathering data about the context of decision making, the types of decisions seniors made, their decisional conflict and the resources they used in decision making. METHODS: Focus groups and a mail-back questionnaire were used. Data were gathered in 1997 from a convenience sample of 50 seniors in Canada who experienced musculoskeletal pain of a noteworthy nature. RESULTS: Findings revealed that participants made decisions within a context of ageing and the health and social consequences of advancing age. The types of decisions they made included to ignore their pain and to use distraction. They also used exercise, the application of heat and cold, and medications to manage pain. Decisional conflict was minimal and consultation with family and friends superseded that with professionals. CONCLUSIONS: This study contributed to knowledge of decision making in later life about health matters generally, and the management and control of pain specifically. Findings point to the resourcefulness of seniors with respect to self-care and decision making. Seniors want to make informed decisions. However, they need information about the risks and benefits of decisions.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.423

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 studies0.0000.000
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 head0.012
GPT teacher head0.324
Teacher spread0.313 · 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 designOther design
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

Citations56
Published2001
Admission routes2
Has abstractyes

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