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Record W7057058047

In the Hands of Strangers: The Myth of Choice and Self-Determination for Chronic Pain Patients in Ontario

2018· other· en· W7057058047 on OpenAlexaboutno aff

Bibliographic record

VenueYorkSpace (York University) · 2018
Typeother
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingAlienationAgency (philosophy)Chronic painTheme (computing)PerceptionHealth careGovernment (linguistics)Chronic disease
DOInot available

Abstract

fetched live from OpenAlex

This study aimed to better understand and explain the lived experience of chronic pain patients within Ontario primary health care settings, their perceptions of self-determination, and the impact on their capacity to self-manage their condition and health-related quality of life. \n \nThrough in-depth interviews with 23 chronic pain (CP) patients, this research establishes an emergent theme of alienation as a key sensitizing characteristic of the experience of the research participants. \n \nSelf-Determination Theory suggests the management of pain is most effective when patients have a sense of agency and some measure of influence in their own health care. This view is consistent with the current provincial government policy of promoting Patient Centred Care (PCC). \n \nThe results of this study suggest that CP respondents experience feelings of alienation in the management of their condition. This experience is evidence of a lack of self-determination created by the inability of the health care professionals to effectively put into practice PCC.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.258
Threshold uncertainty score0.518

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.012
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.177
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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