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Record W4289784934 · doi:10.2196/39617

The Impact of a Text Messaging Service (Tonsil-Text-To-Me) on Pediatric Perioperative Tonsillectomy Outcomes: Cohort Study With a Historical Control Group

2022· article· en· W4289784934 on OpenAlexafffundvenue
Lori Wozney, Negar Vakili, Jill Chorney, Alexander Clark, Paul Hong

Bibliographic record

VenueJMIR Perioperative Medicine · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsDalhousie UniversityIzaak Walton Killam Health CentreNova Scotia Health Authority
FundersDalhousie University
KeywordsTonsillectomyMedicinePerioperativeContext (archaeology)PacuIntervention (counseling)Physical therapyAdenoidectomyNursingSurgery

Abstract

fetched live from OpenAlex

Background Tonsillectomy is a common pediatric surgical procedure performed in North America. Caregivers experience complex challenges in preparing for their child’s surgery and coordinating care at home and, consequently, could benefit from access to educational resources. A previous feasibility study of Tonsil-Text-To-Me, an automated SMS text messaging service that sends 15 time-sensitive activity reminders, links to nutrition and hydration tips, pain management strategies, and guidance on monitoring for complications, showed promising results, with high levels of caregiver satisfaction and engagement. Objective This study aimed to pilot-test Tonsil-Text-To-Me in a real-world context to determine whether and how it might improve perioperative experiences and outcomes for caregivers and patients. Methods Caregivers of children aged 3 to 14 years undergoing tonsillectomy were included. Data from a historical control group and an intervention group with the same study parameters (eg, eligibility criteria and surgery team) were compared. Measures included the Parenting Self-Agency Measure, General Health Questionnaire-12, Parents’ Postoperative Pain Measure, Client Satisfaction Questionnaire-8, and engagement analytics, as well as analgesic consumption, pain, child activity level, and health service use. Data were collected on the day before surgery, 3 days after surgery, and 14 days after surgery. Participants in the intervention group received texts starting 2 weeks before surgery up to the eighth day after surgery. Descriptive and inferential statistics were used. Results In total, 51 caregivers (n=32, 63% control; n=19, 37% intervention) who were predominately women (49/51, 96%), White (48/51, 94%), and employed (42/51, 82%) participated. Intervention group caregivers had a statistically significant positive difference in Parenting Self-Agency Measure scores (P=.001). The mean postoperative pain scores were higher for the control group (mean 10.0, SD 3.1) than for the intervention group (mean 8.5, SD 3.7), both of which were still above the 6/15 threshold for clinically significant pain; however, the difference was not statistically significant (t39=1.446; P=.16). Other positive but nonsignificant trends for the intervention group compared with the control group were observed for the highest level of pain (t39=0.882; P=.38), emergency department visits (χ22=1.3; P=.52; Cramer V=0.19), and other measures. Engagement with resources linked in the texts was moderate, with all but 1 being clicked on for viewing at least once by 79% (15/19) of the participants. Participants rated the intervention as highly satisfactory across all 8 dimensions of the Client Satisfaction Questionnaire (mean 29.4, SD 3.2; out of a possible value of 32.0). Conclusions This cohort study with a historical control group found that Tonsil-Text-To-Me had a positive impact on caregivers’ perioperative care experience. The small sample size and unclear impacts of COVID-19 on the study design should be considered when interpreting the results. Controlled trials with larger sample sizes for evaluating SMS text messaging interventions aimed to support caregivers of children undergoing tonsillectomy surgery are warranted.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.028
GPT teacher head0.419
Teacher spread0.391 · 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.

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

Citations3
Published2022
Admission routes3
Has abstractyes

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