1. ABOUT THE DATASET -------------------- Title: "This completely misunderstood riddle": menstrual health in young people's mental health services Creator(s): Fabiola Guerra Organisation(s): University of Leeds Rights-holder(s):Unless otherwise stated, Copyright 2025 University of Leeds Publication Year: 2025 Description: This dataset contains 13 anonymised, verbatim transcripts of semi‑structured interviews conducted with mental health professionals who have experience of clinical work with young people in community settings. The interviews explored professionals’ perspectives on menstrual health, including how professionals engage and understand menstrual health in their clinical practice. Interviews were conducted remotely, recorded with consent, and transcribed by the researcher. Each transcript is provided as an individual Word file labelled with a participant pseudonym, and all identifying information has been removed. The dataset was generated as part of the author’s Doctorate in Clinical Psychology (DClinPsy) research project at the University of Leeds. The dataset may be relevant to researchers interested in menstrual health, clinical work, professionals perspectives, qualitative methods, health services, gender studies. Cite as: Guerra (2025) Dataset for '"This completely misunderstood riddle": menstrual health in young people's mental health services'. University of Leeds. Dataset https://doi.org/10.5518/1788 Related publication: Guerra, F. (2025). "This completely misunderstood riddle": menstrual health in young people's mental health services. Doctorate in Clinical Psychology, University of Leeds. White Rose eTheses Online. Status at time of deposit: Published. Available at: https://etheses.whiterose.ac.uk/id/eprint/37652/ Contact: fabiolaguerra.fg@gmail.com 2. TERMS OF USE --------------- Copyright 2025, University of Leeds, Guerra. Unless otherwise stated, this dataset is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/. 3. PROJECT AND FUNDING INFORMATION ---------------------------------- Title: "This completely misunderstood riddle": menstrual health in young people's mental health services Dates: Data collection took place between September 2024 and November 2024. The research project was conducted between 2024 and 2025. Funding organisation: This research was carried out as part of the Clinical Psychology Doctorate (DClinPsy) at the University of Leeds and did not receive external project‑specific funding. Grant no.: Not applicable. Funding statement: This dataset was not created in the course of a funded project. It was generated as part of the author’s doctoral training research within the University of Leeds. 4. CONTENTS ----------- File listing This dataset contains 13 anonymised interview transcripts, each stored as an individual Microsoft Word document. Every file corresponds to one participant and is labelled using the assigned pseudonym. The used pseudonyms are different colours (e.g. "Blue" is the pseudonym for one participant, "Green" the pseudonym for another participant). All transcripts have been checked for accuracy and cleaned to remove identifying information. Transcription includes notes indicating pauses, emphasis, or relevant contextual details where appropriate 5. METHODS ---------- The dataset was generated through semi‑structured qualitative interviews with mental health professionals who had experience working clinically with young people in community settings. Interviews explored professionals’ perspectives on menstrual health, including how menstrual experiences are understood, assessed, and integrated into clinical work. Interviews were conducted remotely via video call, using Microsoft Teams. Each interview followed a semi‑structured schedule, allowing consistent coverage of core topics while enabling participants to elaborate freely. Interviews lasted from 30 to 60 minutes, with an average lenght of 41 minutes; the total amount of data was 519 minutes. Video recordings were transcribed verbatim by the researcher (using the Microsoft Teams automated trascriptions as a starting point). Transcripts were anonymised by removing names, locations, services, and any other identifying details.