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Record W4401819436 · doi:10.25259/ijpc_13_2024

A Multicentric Field Test to Study the Validity and Feasibility of the SHS-tool to Screen for Serious Health-related Suffering in Adult Patients with Cancer

2024· article· en· W4401819436 on OpenAlexaff
Nandini Vallath, Aneka Paul, Arunangshu Ghoshal, Jenifer Jeba Sundararaj, Kalpana Balakrishnan

Bibliographic record

VenueIndian Journal of Palliative Care · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsCronbach's alphaMedicineTest (biology)Health careGovernment (linguistics)CommissionFamily medicineNursingPsychometricsClinical psychologyEconomic growth

Abstract

fetched live from OpenAlex

Objectives: The 2017 Lancet Commission reports ‘Serious Health-related Suffering’ (SHS) as an abyss in healthcare services. It lists 20 common health conditions and 15 symptoms as commonly associated with SHS. In 2015, 80% of SHS prevalence, an estimated 61 million, was noted as from low-middle-income countries. Acknowledging the high prevalence of SHS in cancer patients and aligning with global efforts to address and alleviate the suffering, the National Cancer Grid of India developed and evaluated the SHS screening tool (SHS-tool). The SHS tool was developed during phase 1 of the study through a systematic consensus-building methodology. During phase 2, the validity and feasibility study of the SHS tool was completed through a multicentric field test, which is described here. Materials and Methods: The SHS tool developed during phase 1 was field-tested across nine tertiary cancer care centres (TCC sites) selected from different healthcare sectors and regions of India. The study utilised a purposive sample of 254 cancer patients to evaluate the validity of the SHS screening tool at selected sites and additionally recorded the feasibility, relevance, acceptability and feedback comments from patients ( n = 121), research associates ( n = 11) and principal investigators (PIs) ( n = 9). A documented interview of the patient within the same timeframe by experienced personnel selected by the PI served as the standard. Results: The field-test TCC-sites represented government academic institutions, non-government and private sectors. The sites used patient waiting areas and inpatient/daycare wards for conducting field tests. The Cronbach’s alpha of the SHS-tool questionnaire showed an internal consistency of 0.728. The tool detected SHS in 137/254 patients, compared to 116/254 through the interview method. The outcomes concurred with that of the interview in 64.17% of instances. The tool exhibited a sensitivity of 70% and specificity of 59%. 66.67% of patients might not have reached the interviewers if not for the field test processes. The feasibility questionnaire responses from patients ( n = 121) indicated ease of understanding (91.74%), ease of use (92.56%) and relevance (89.26%). The selected settings were found suitable by 96.69%. Feedback responses from research associates indicated ease of administration (10/11) and relevance (8/11) and found no reasons preventing its use (8/11). The feedback comments from the stakeholders were thematically grouped for insights. Conclusion: The SHS tool is validated for screening SHS where none exists. It has been found to be a feasible, relevant and acceptable tool for use in adult cancer patients attending TCCs across India. Insights from analysing the feedback comments from the stakeholders have been integrated as ‘instruction for use’ for refined implementation of the SHS tool. The SHS tool may be utilised to recognise and trigger an in-depth evaluation and expedited access to essential palliative care packages towards alleviating it, as recommended by the Lancet Commission. Future studies using the SHS tool in other disease conditions with a high burden of SHS can assess its wider applicability.

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)0.002
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 score0.056
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.095
GPT teacher head0.423
Teacher spread0.328 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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