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Record W2172171881 · doi:10.2196/mhealth.4649

The Development of the Recovery Assessments by Phone Points (RAPP): A Mobile Phone App for Postoperative Recovery Monitoring and Assessment

2015· article· en· W2172171881 on OpenAlex
Maria Jaensson, Karuna Dahlberg, Mats Eriksson, Åke Grönlund, Ulrica Nilsson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR mhealth and uhealth · 2015
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsnot available
FundersForskningsrådet om Hälsa, Arbetsliv och VälfärdÖrebro Universitet
KeywordsmHealthMobile phoneMedicinePhoneCohortMedical emergencyPatient satisfactionAdverse effectNursingComputer sciencePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: In Sweden, day surgery is performed in almost 2 million patients per year. Patient satisfaction is closely related to potential adverse events during the recovery process. A way to empower patients and give them the opportunity to affect care delivery is to let them evaluate their recovery process. The most common evaluation method is a follow-up telephone call by a nurse one or two days after surgery. In recent years, mHealth apps have been used to evaluate the nurse-patient relationship for self-management in chronic diseases or to evaluate pain after surgery. To the best of our knowledge, no previous research has explored the recovery process after day surgery via mobile phone in a Swedish cohort. OBJECTIVE: The objective of the study is to describe the process of developing a mobile phone app using a Swedish Web-based Quality of Recovery (SwQoR) questionnaire to evaluate postoperative recovery after day surgery. METHODS: The development process included five steps: (1) setting up an interdisciplinary task force, (2) evaluating the potential needs of app users, (3) developing the Swedish Web version of a QoR questionnaire, (4) constructing a mobile phone app, and (5) evaluating the interface and design by staff working in a day-surgery department and patients undergoing day surgery. A task force including specialists in information and communication technology, eHealth, and nursing care worked closely together to develop a Web-based app. Modifications to the QoR questionnaire were inspired by instruments used in the field of recovery for both children and adults. The Web-based app, Recovery Assessment by Phone Points (RAPP) consists of two parts: (1) a mobile app installed on the patient's private mobile phone, and (2) an administrator interface for the researchers. RESULTS: The final version of the SwQoR questionnaire, which includes 31 items, was successfully installed in RAPP. The interface and the design were evaluated by asking for user opinions about the design and usefulness of the app with 10 day surgery patients. Some minor adjustments were made concerning text size and screen color. CONCLUSIONS: Taking advantage of joint expertise, a useable Web-based app adaptable to different technical platforms was constructed. In addition, the SwQoR was successfully transferred into digital format for use on mobile phones.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.071
GPT teacher head0.417
Teacher spread0.346 · 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