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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".