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Record W4393927414 · doi:10.2196/preprints.58999

Applying fit theory: exploratory analysis of an online survey of care coordination and satisfaction with ambulatory cancer care during the COVID-19 pandemic in Manitoba, Canada (Preprint)

2024· preprint· en· W4393927414 on OpenAlexaboutno aff
Maclean Thiessen, Andrea Soriano, Jason Park, Kathleen Decker

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakExploratory analysisExploratory researchSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyGerontologyMedicineSociologyComputer scienceVirologyData scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND During the COVID19 pandemic in Manitoba, Canada, the cancer experience was explored using two sibling studies. One study consisted of an online survey designed to assess how patient satisfaction and experience with care coordination differed among the population of cancer patients undergoing cancer treatment. The survey facilitated recruitment for a grounded theory study exploring the cancer experience and how the pandemic impacted it. OBJECTIVE This report presents the results of the survey, with discussion informed by the findings of the grounded theory study. METHODS A link to an online survey was made available to patients receiving cancer treatment (intravenous treatments and radiotherapy) in Manitoba, Canada, between July 31, 2020, and February 28, 2022, primarily through invitations printed on patient’s individualized treatment schedules. The survey included validated patient reported experience measures (PREMs) for patient satisfaction and care coordination as well as an option to opt into being contacted for additional research opportunities. Analysis included the generation of descriptive statistics and logistic regression, including univariate and stepwise multivariate model building, exploring predictors of above and below average PREMs scores. RESULTS A total of 203 responses were collected, 154 were complete for at least one PREM measure and were included in the analysis. Average age was 65 years (SD = 11.7). Most respondents were male (n = 79, 52.7%), and being treated with curative intent (n = 81, 53.6%). The most common type of cancer was breast (n = 41, 26.6%). Univariate analysis demonstrated that age 60 – 69 was associated with above average satisfaction with care (OR = 2.205, 95% CI = 1.045 – 4.624, P = .04), while age < 60 (OR = 0.437, 95% CI = 0.204 – 0.934, P = .03) and ECOG ≥ 2 were associated with below average patient satisfaction (OR = 0.327, 95% CI = 0.137 – 0.782, P = .01). Age between 60 – 69, ECOG ≥ 2, and hematological malignancy were selected through stepwise model building, resulting in an explanatory model (R2 = 0.129) of patient satisfaction. ECOG ≥ 2 was associated with below average care coordination (OR = 0.357, 95% CI = 0.145 – 0.880, P = .03), and was the only identified predictor of care coordination, with no explanatory multivariate model generated. CONCLUSIONS This survey identified that those with poor functional status, as well as those outside of the 60 – 69 age range, are likely to have a below average experience with regards to satisfaction and coordination of care. Through the lens of the sibling grounded theory study, it is possible that these findings are due to unmet supportive care needs that need to be further characterized and addressed. The approach to collecting PREMs used in this study was simple to deploy, and yielded meaningful results, however further work is needed to improve response rates.

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)0.014
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.832
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.048
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.009
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.165
GPT teacher head0.420
Teacher spread0.255 · 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 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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